As the most recent surge of COVID consumes Florida, there is a lot of talk about the ‘ICU’ in hospitals – namely, how the proportion of ICU COVID patients is lower now than it has been in the past or that the rise in ICU numbers have not kept pace with overall hospitalizations as it had previously. You’ll probably even hear respected scientists, doctors, and journalists say “well, cases are ridiculously high and hospital numbers are at crazy, unsustainable levels – but ICU is increasing slower than previous waves so don’t worry about it! (It’s milder!)”
While these ICU factoids are true from a literal data standpoint, there is no evidence that ICU levels are the primary indicator of what is occurring in the real world (at least in Florida). That is, the number of “covid patients in ICU” may seem like it would be the best metric of the severity of the pandemic (as measured by deaths, for example), but the data just does not support this.
In other words, malicious hacks like Kyle Lamb (who seem to be recruited by the DeSantis Regime solely to spread misinformation and harass anyone who dare talk about Florida COVID data) are full of nonsense, desperately trying to distract from the disaster unfolding in Florida. But, unlike the fascists running the state of Florida whose only concern is crafting a PR message to enact an extreme political agenda, I prefer to do the opposite.
Regression Models for Deaths
Firstly, what is the ‘ICU?’ As a layman with almost no knowledge of medicine or hospitals, I think of it as a ‘hospital within the hospital’ where the most severely wounded or ill end up. For COVID patients, I think of ventilators and intubation – basically, where people go to die. Naturally, because most people have only a general idea of what ‘ICU’ is (completely ignoring the minutiae of hospital logistics, data definitions, reporting of data across thousands of hospitals, etc.), simplistic messages like this or this imply that the situation is less severe because ‘less ICU = less bad.’ Hard to argue against that, right?
As an alternative to using ICU numbers, we start with an obvious choice: overall hospitalizations. Conceptually, I prefer the more general hospitalizations to ICU numbers because it presumably has fewer levels of bureaucracy to pass through. In addition to worrying about the definition of ‘covid hospitalization’ (don’t get me started on the FOR/WITH COVID talking point that is making the rounds again), you must also consider what constitutes ‘ICU.’ Generally, the fewer assumptions, caveats, and political meddling, the more reliable and consistent the data will be.
To see which explains severity better (as measured by 7-day average of hospital reported deaths) and determine if the proportion of ICU patients has any effect on expected deaths, Poisson regression models were compared using overall census (along with % in ICU) and ICU census. The coefficient estimates for the Overall and ICU models are shown in tables 1 and 2, respectively. The number of days lag for each model was chosen by whichever value (from 0 to 10 days lag) minimized model deviance.
Based on goodness of fit over the past six months (covering the entire Delta Summer 2021 wave and into the current one), the overall hospital census is better than ICU census at explaining the ‘severity’ within hospitals. In addition, the percent of all COVID patients that are in ICU (the ‘% ICU, 7 Day Lag’ parameter in Table 1) has no significant effect on expected deaths, thus making all ‘% in ICU’ (and to a lesser extent, total ICU) talking points moot.
Table 1. Poisson regression coefficient estimates for ‘Census Model’ using overall census and percent within ICU (with autocorrelation consistent robust standard error estimates)
Log(Weekly Average Census, 7 Day Lag)
% ICU, 7 Day Lag
Null deviance: 16549 on 192 df; Residual deviance: 36.35 on 190 df; AIC: 1151.1
Table 2. Poisson regression coefficient estimates for ‘ICU Model’ using ICU census (with autocorrelation consistent robust standard error estimates)
Log(Weekly Average ICU, 5 Day Lag)
Null deviance: 16549 on 192 df; Residual deviance: 116.38 on 191 df; AIC: 1229.1
As we can see from the figure, the ‘ICU model’ (green line) fits the Delta peak of deaths quite poorly and appears to be severely underfitting the current increase while the overall ‘Census Model’ (red line) fits much better throughout and continues to track the current wave’s increase well. This is not saying that the number of ICU patients is not predictive (it clearly is based on the model in Table 2), but it is not as accurate as the overall number of people hospitalized.
There are likely more restrictive limits to ICU utilization vs overall hospital (staff, beds, etc.) so its use may be limited in certain situations when hospitals are overwhelmed or there are staff shortages. I would not be surprised if political pressure is being applied to make the ICU situation appear rosier than reality; it wouldn’t be the first time ICU definitions were changed in Florida.
But, looking only at the data, there’s no evidence that the proportion of ICU is indicative of the general severity of patients or the number of deaths in hospitals (in Florida using HHS data). If this were the case, you would expect deaths to increase as % ICU increased for a given number of overall hospitalizations; this is not observed, however. Of course, these relationships might change in the future, but for the past 6 months, ‘% ICU’ is a mostly useless variable when modeling deaths. Also, these models say nothing about hospital resources in ICU vs non-ICU beds (a separate but important consideration), only about expected hospital deaths. It might be of interest to include a time factor (either linear or a Delta vs Omicron factor) to evaluate if the expected number of deaths (for a given census) is changing over time.
In conclusion, do not be tricked into believing there is no crisis because of uncritical, conventional wisdom (‘less ICU = less bad’) or many-people-are-saying-type narratives – look at the data and see what is happening yourself! Based on current trajectories of hospital admissions, this wave will likely approach (if not exceed) that of Summer 2021 in terms of overall hospitalizations and possibly even deaths, sadly. This is not being a ‘doomer’ or having a ‘political agenda.’ It is simply being a realist that is willing to confront inconvenient truths (like the very real prospect of another 20,000 Floridians dying over the next few months). If it turns out that this wave is less severe, and many people are only incidentally hospitalized – great! Prepare for the worst but hope for the best. We all want to get back to normal but should do so safely and smartly.
But remember: the greatest harm in being cautious – or dare I say, conservative – is damaging the egos of sociopaths like Ron DeSantis who desperately want you to believe that everything is fine so he can have yet another Victory Tour on Fox News at your expense. The alternative, being recklessly optimistic, could result in thousands of preventable deaths of society’s most vulnerable – something DeSantis and his army of keyboard warriors are more than willing to risk if it means gaining political power.
If you’ve been following the coronavirus situation in Florida and around the world, you’re probably aware of the rise in cases nearly everywhere – most likely the result of the highly contagious Omicron variant.
Florida’s rapid rise in cases has been especially dramatic, going from one of the least infected states (per the state’s own data, which is problematic, but you already know that) just weeks ago to currently among the worst in the USA with little sign of slowing.
The most recent 7-day average is nearly 4 times greater than just a week before – going from 5,320 cases/day on December 17 to 21,125 on December 25. Since recording an average of about 2000 cases/day on December 11, the 7-day average has increased more than 10 times in two weeks.
Now these numbers may seem frightening on their own – and they are for many reasons – but the immediate concern is managing hospital resources. How has the most recent surge effected hospitals so far?
Admissions, hospital census, and hospital deaths within hospitals are all increasing, but not nearly in proportion to earlier waves of infection.
In previous waves, cases and hospital admissions moved closely in tandem (along with hospital census and deaths within hospitals). Within the past few weeks, however, there is a distinct divergence between cases and hospital numbers.
This could be due to widespread recently acquired immunity during Florida’s deadly Delta wave in Summer 2021 or possibly lower severity of the Omicron variant. It may be in part due to the outbreak being thus far concentrated in the most vaccinated areas of Florida – Miami-Dade, Broward, and Palm Beach counties (more on this later) – or in younger age group. It could also be in part due to the extremely rapid spread, where hospital admissions need more time to “catch up” to the explosion of cases.
At this point, just a few weeks into the Omicron wave, it is more difficult to determine the new relationship between cases and hospital metrics. It is likely that the risk of hospitalization (per case) is lower in this wave than the previous Delta wave due to more milder cases in those who are vaccinated. During the Summer 2021 wave, daily hospital admissions were about 10-15% of the total cases (7-day average). Now, this number is trending much lower, with daily admissions only about 2% of total cases.
That is the good news. The bad news is that hospitalization numbers are projected to rise along with admissions – much more rapidly than the previous two weeks. Using today’s admissions, we can get a fairly accurate prediction of the hospital census a week or so out (see the graph above where the blue ‘Admissions’ line is nearly identical to the black ‘Census’ line, shifted several days back).
Between December 12 and December 26, the 7-day average hospital census increased from 1,408 to 2,108, or a 50% increase. However, by the new year (just six days between December 26 and January 1), the hospital census is expected to increase by nearly 80%, going from 2,108 to almost 3,800.
With cases and admissions still increasing, this estimate for January 1 is already almost a quarter of the worst of Summer 2021 that resulted in over 17,000 active hospitalizations and 400 deaths/day at its peak. Although the risk of hospitalization appears lower for this wave, the death rate of those hospitalized is unknown currently.
Making matters worse is the location of the current outbreak: relatively highly vaccinated South Florida (although it is difficult to know the true level of vaccination in this area due to the large number of vaccines given to non-residents that is not tracked by Florida DOH, and Florida’s previous over-counting of vaccinations).
Current hospitalization predictions may be optimistic in that the cases are disproportionately from counties with presumably high vaccination rates. Once spread continues into regions with less immunity, the ratio of hospitalization to case rates will likely increase. Combined with Florida’s below average boosting rates, there will be tough times ahead in 2022.
Hospital admissions are calculated using the sum of “previous_day_admission_adult_covid_confirmed” and “previous_day_admission_pediatric_covid_confirmed.” Hospital census is “inpatient_beds_used_covid.”
As the COVID-19 pandemic begins to end and the political/media hacks start discussing which politicians ‘won’ the pandemic, it is important to remember the unfortunate reality that some states are better equipped to handle a public health crisis. Because of this, metrics used to compare a state’s ‘performance’ would be more informative if adjusted by prior expectations. In this way, we can attempt to measure states’ relative effectiveness of direct mitigation measures rather than pre-existing factors that were in place prior to the pandemic. In short – how much of a state’s success is due to its response and how much is due to its inherent characteristics?
Let’s be honest: nobody expects a state like Mississippi to have a lower death per capita than, say, Vermont – even with a flawless pandemic response. Social, demographic, and political factors greatly affect a state’s health outcomes, perhaps more than a coherent pandemic strategy.
For example, a state with a higher obesity rate would expect to have higher rates of severe illness and death given the same rate of infection (with all other factors being equal). The state with higher obesity may have an overall higher rate of death, but after adjusting for the population’s rate of obesity, the two might have performed equally well in their response to the crisis. If Mississippi were to end the pandemic with an equal death rate to Vermont, it would be fair to say Mississippi performed ‘better’ having overcome so many disadvantages.
Adjusting for Pre-Existing Conditions
Firstly, what factors would determine a state’s health outcomes independent of its actual pandemic response? Even with just the most cursory knowledge of epidemiology, it is not difficult to make an educated guess. Some characteristics that would intuitively seem important are general health of the populace (e.g., obesity or mortality rates), demographics (e.g., proportion of elderly or percent of population in nursing homes), geography (e.g., population density or climate), and social factors (e.g., political ideology, education level, and poverty).
The outcome measured is a modified version of the reported deaths per million residents. Firstly, this modified metric only includes deaths reported after June 1, 2020 (through September 30, 2021) so that the analysis focuses on states’ response after the initial wave (i.e., after effective treatment and vaccines were developed). Secondly, the metric adds a certain number of cases as equivalent deaths – 500 cases equal to one death, specifically. That is, it is the number of deaths reported since June 1, 2020 in addition to the number of non-lethal cases reported since June 1, 2020 (the number of cases less the number of deaths reported) divided by 500. The rationale for incorporating cases is that a certain proportion of non-lethal cases will likely result in reduced lifespan/long-term quality of life. At the very least, there should be some penalty for temporary reduction in productivity that should be considered when evaluating performance.
As a first attempt and proof of concept, a multiple linear regression was fit to adjust for these factors to ‘score’ each state’s mitigation strategy. The residuals – the difference between the expected and actual deaths per million – can be thought of as some combination of 1) effect of state characteristics omitted/not captured in the model, 2) effect of mitigation strategy and 3) random error. (There is, of course, some additional uncertainty in the deaths per million outcomes as not all states have the same testing and death-reporting standards; however, we’re going to assume there is no widespread and significant misreporting of cause of death by any state. Excess deaths would likely be a more accurate measure so perhaps this will be explored later.). The ‘response’ of a state includes things such as school policy, mask use, vaccine administration, limitations on capacity, etc.
All explanatory variables were centered and scaled (with mean of 0 and standard deviation of 1) to compare the relative influence of each factor on death rates. One well-fitting model (referred to as ‘A’) that includes factors for each of the five categories mentioned is:
The magnitude of the estimates indicates the relative influence of each parameter. The percent of population in nursing homes has the most effect, followed by obesity rate, poverty rate, relative humidity, and per capita healthcare expenditure. As obesity, poverty, and the percent of population within nursing homes increases, so does the expected death rate, while an increase in healthcare expenditure and relative humidity is associated with a decrease (and vice versa). For example, an increase in 1 standard deviation of the obesity rate (or about 3.8 percentage points) increases the average deaths per million by 301. For every increase in 1 standard deviation in per capita healthcare spending (or $1,257 per person), states saw an average decrease in 169 adjusted deaths per million.
(It should be noted that the inclusion of median age, proportion of population 65 or older, and proportion 80 or older do not result in a better model fit than any of these variables. As we will see later, population age – a very popular but inaccurate talking point about Florida – is of relatively low importance to overall death rate).
In set A, the five states with the largest positive residuals are Florida, Montana, Massachusetts, Georgia, and Rhode Island. The five states with largest negative residuals are Nebraska, Hawaii, Maine, Vermont, and West Virginia. Florida’s residual (904 deaths/million greater than expected) would translate into more than 19,400 deaths greater than expected. We could easily stop here and lazily conclude that ‘Florida is the worst when adjusting for X’ but we have standards here. Here is another one:
Deaths per million = 2095 – 335 (High School GraduationRate) – 244 (Pct Biden Vote) + 172 (Hospital Beds per Capita) + 133 (Median Air Quality Index) – 202 (State Expanded Medicaid)
In this set, referred to as ‘B,’ high school graduation rate is most influential, followed by percent of Biden vote, per capita hospital beds, and median air quality (state expanded Medicaid is a binary variable so not exactly comparable to the others). The interpretation is the same as set A; a one standard deviation increase in high school graduation rate (or 2.7 percentage points) decreases the expected deaths per million by 335, and so forth. Adjusting for all other variables, states that chose to expand Medicaid saw, on average, 202 fewer deaths per million that states that did not.
Adjusting for these variables, the five states with the largest positive residuals are Rhode Island, Montana, Arizona, Massachusetts, and Iowa. The five states with the largest negative residuals are California, Maine, Utah, Kentucky, and West Virginia. California’s residual (659 deaths/million less than expected) would translate into 26,000 deaths ‘prevented.’
These plots show each state’s expected value against the actual observed value for both sets of variables. An observed value above (below) the diagonal line may suggest a ‘worse’ (‘better’) response. With an R2 value of 0.68 and 0.77 for sets A and B, respectively, we can safely conclude that a large majority of the total variation in death rate can be explained with just a handful of pre-existing, uncontrollable factors. The current leader in per capita deaths, Mississippi, probably never had a chance at avoiding a terrible death toll while a state like Vermont was set up for success decades or even centuries ago.
It should be stressed that the above linear models are just a few of many plausible ones. If variables were changed or omitted or transformed, we would get a different residual for each state. Using another statistical model besides linear regression would also change the results. The goal is to get an average residual for each state across several well-fitting models, as well as discovering which characteristics are most associated with a state’s performance. The ‘true’ effect of a state’s characteristics is not likely captured by the two above equations, so it is best to get input from a diverse set of models that can handle complex interactions between variables.
To achieve this, five different models were used: gradient boosting machine (GBM), random forests (RF), support-vector machine (SVM), LASSO, and an ensemble of best subset linear models (BSLM). Although prediction is not the purpose, ten-fold cross-validation was performed to tune some of parameters in the GBM and SVM to avoid extreme over-fitting. To select the best subset of variables in BSLM method, 2000 random subsets of between one and five variables were sampled, with the fitted values of the best 20 unique subsets averaged (measured by AIC, with any variable with maximum VIF>5 iteratively removed and refit). To get a distribution of each state’s residual, this process was repeated with 500 bootstrap samples for each model, or 2500 bootstrap samples in total.
The basic theory is that a consistent deviation from the expected death rate across several models (and many thousands of resampling of the data to achieve slight variations in the parameter estimates and thus residuals) is most likely an effect of the state’s response. Florida, for example, had a negative residual across the 2500 bootstrap samples less than 1% of the time. The plot below shows the distribution of the bootstrap residuals for each state.
Florida still has one of the highest residuals when averaged across all models, currently in third-worst place behind Arizona (by far, the worst state thus far) and Rhode Island; Florida is likely to have moved into second worst since this is published. On the other end of the spectrum there are the ‘usual suspects’ – Hawaii, Vermont, and Maine in first, second, and third place, respectively. Other states of note are California (18th best), New York (26th best), and Texas (31st best), all at about average performance (which is not unexpected in large, diverse states). Despite vastly different raw deaths per million, New York and Louisiana (25th) are scored about the same in this analysis, with Louisiana just a slightly more negative average residual. That is an admirable result for Louisiana considering the consecutive brutal hurricane seasons and the relative performance of its peer states.
This ordering looks a lot like the unadjusted ranking of post-June 2020 deaths per million. This isn’t surprising given that a significant amount of the variability of death rate can be explained by pre-existing factors (>70% very easily achieved with a simple linear model, with probably some confounding of actual policy within the variables included); there is only so much a state can overcome (or screw up). Louisiana, for example, is fifth highest in post-June death rate – but that is exactly what is expected when adjusting for its characteristics, hence the average ranking of performance. Rhode Island and Massachusetts, despite doing quite poorly by their own standards, are still only mediocre at 33rd and 24th lowest in unadjusted death rate, respectively. West Virginia (34th lowest unadjusted to 22nd) and Kentucky (30th to 11th) were big movers in the good direction. Perhaps coincidental, but Jim Justice (governor of West Virginia) and Mitch McConnel (Pale Man of Kentucky) have been some of the least insane Republicans dealing with COVID-19on .
These results seem to show how little government policy seems to affect this outcome beyond expectations. After nearly two years, two administrations, and a lot of confusing messaging from the government and disinformation from right-wing media, there is only so much a government can do to influence the behavior of millions of people. Imagine the nightmare of overseeing California or Texas (or any large, diverse state) and having to tailor your response to all the diverse subpopulations living there. However, this isn’t a suggestion to have a ‘freedom over fear’ policy because many thousands of deaths are probably avoidable – especially in reckless states like Florida with a large population. Small nudges to incentivize or discourage behavior can have great compounding effects.
Paradoxically, it seems that states with less need for mandates and government intervention adopt more strict policies while states that could benefit from a more ‘heavy-handed’ approach tend to eschew precautions. This is a classic example of confounding in that a state’s characteristics affect both its policies and the actual outcome itself. For example, states that opted into Medicaid expansion are more likely to mandate masks in schools, thus complicating some of the inference. Nevertheless, the ability to successfully respond to a crisis is very much determined by the cumulative effect of thousands of decisions of voters and its government over decades.
If Louisiana behaved more like Florida or Arizona, thousands of more people would likely be dead today. Conversely, if Florida had just met expectations, 10000 more people would possibly be alive today (average residual of 476 deaths/million x 21.5 million). California, although just slightly above average performance, may have prevented the most deaths (nearly 5000) due to its size, despite moderate success. By this analysis, Ron Desantis has overseen more preventable deaths than any other state government – and by a wide margin.
Mean Residual (deaths/million)
Total Deaths Caused (+)/Prevented (-)
Mean Residual (deaths/million)
Total Deaths Caused (+)/Prevented (-)
Important State Characteristics
Which factors determine each state’s performance? The table shows a very crude and simplistic estimate of the relative importance of each of the variables considered. ‘100’ means that this variable was most important within the model (as defined by some criteria, such as mean decrease in prediction accuracy when removed), with all other ones being scaled to this maximum value. Note that difference in criteria and nature of each algorithm make these not exactly comparable (as presented). Comparing absolute values between methods should not be done, but within a method relative to other variables. For example, Incarceration Rate is 60% as important as Percent of Population with High School Diploma within the GBM, while this ratio is 95% within the Random Forest. The LASSO model performs variable selection, making many variables have zero influence, while SVM and RF have more equal influence among all variables.
We’ll go through a handful of the most important variables and try to analyze why each might affect death rates or, at the very least, be associated with them. Most of these are self-explanatory, as they simply tell the story of American inequality and poverty. They are all correlated and are a cause as well as an effect on each other (like education level and incarceration). Or they could simply be a sort of proxy variable that only correlates with the true effect that causes death. Either way, they can be used as an affective adjustment method.
1. Pct Pop. High School Diploma
As we can see from the table, the best predictor of a state’s death rate is the percent of a state’s population that has graduated high school. Remember – this is not necessarily causal, but more likely indicative of the social, political, and cultural aspects of a state. If you don’t have a high school diploma, you’re more likely to be poor, lack healthcare, be non-white, or work in a job that cannot be done remotely (or get sick leave, paid vacation, etc.). High school dropouts are also more likely to end up in prison.
2. Incarceration Rate
This likely has a causal component (prisons and jails are covid hot spots) so having more of the population locked in confined spaces – usually with inadequate healthcare – will increase the death rate. Predictably, states with the higher percentage of minorities (higher mortality) tend to have higher rates of incarceration. States with a high incarceration rate are, perversely, more likely to adopt a ‘freedom’ strategy bans mask and vaccine mandates, i.e., anything that might take societal cooperation or compassion for others. Only in America, right?
3. Pct Pop. Bachelor’s Degree
This is similar to the percent of people with high a school diploma. College graduates earn more money on average, along with all the benefits associated with higher income and more often a ‘white collar’ profession (this is defined as employees in the ‘Information,’ ‘Finance and Insurance,’ and ‘Professional, scientific, and technical services’ categories from the Bureau of Labor Statistics). College graduates are probably also less likely to be susceptible to Fox News propaganda and Facebook posts from your crazy right-wing uncle. They are more likely to live in higher-taxed states with more investment in social and healthcare spending. Also, they are less likely to smoke, be obese, or have chronic health problems like diabetes. Stay in school, kids!
4. Hospital Beds per Capita
You might think that having a high number of hospital beds would be good during a pandemic – and it might be in certain situations – but it is associated with a higher death rate. Being a business, hospitals have more beds in states where there is higher demand, driven by greater number of unhealthy people (or extremely old and frail, i.e., those in nursing homes).
5. Percent Biden Vote
This one has been discussed at lengths elsewhere. Democrats are more likely to be vaccinated, wear a mask, and take preventative measures in general. They also tend to not ridicule, threaten, or be dismissive of scientists who have been studying these sorts of things for decades. They probably do the opposite of whatever ‘don’t Fauci my Florida’ is. Dying to own the Libs!
6. Nursing Home Residents Per Capita
Like prisons and jails, covid entering a nursing home will almost assuredly end in disaster (barring wide-scale vaccination). The more people living in nursing homes, the higher the expected death rate. Other than this seemingly causal link, states with higher percentage of population in nursing homes also tend to have more chronic conditions, be elderly, and more Republican – all of which are hazardous to one’s health.
I think you get the idea: states with generally worse quality of life and socioeconomic conditions have fared worse than those with better ones. In other words, COVID-19 is just the latest massacre in America’s Class War – and the poor, vulnerable, and misguided (of all races) have paid the price once again.
The Myth of Population Age
With the recent dramatic rise of Florida in the ranks of death rate, a popular talking point has emerged: “Actually, Florida has a lower death rate than California/Blue Boogeyman State when adjusting by age of population.” (They really like to compare everything with California, for some reason).
This might make sense if age-weighted death rate was not just an artificial statistic with no intrinsic value, but it is merely an index that alone is insufficient at accurately comparing states. Invoking ‘age-adjustment’ might make sense if population age was the primary predictor of population death rate, but it’s not. As seen in the table of variable importance, none of the three age variables – median age, percent of population 65 or older, and percent 80 and older – are anywhere near the top for any model.
Going back to the best subset linear models, let’s see how often age variables produce the best-fitting models. 50,000 unique random subsets of between one and five variables were chosen and the 100 unique subsets with lowest AIC were kept (any variable with maximum VIF>5 was iteratively removed and remaining variables refit). Pct Pop 80 or Older, Pct Pop 65 or Older, Median Age, and Pct Pop in Medicare were in just 5%, 4%, 2%, and 2%, respectively, of the best 100 subsets (compared to 93% with Highschool Graduation Rate, 64% with Per Capita Hospital Beds, and 48% with Percent Biden Vote). Their inclusion could be nothing more than random chance. LASSO does not select any of the four age-related variables mentioned, either.
While age may be an individual-level risk factor, there is no evidence that the population’s age distribution has any effect on the overall death rates, which contradicts the entire rationale for age-weighting (which is, the older the population, the more deaths are expected so this needs to be adjusted for). In fact, having an older population may be advantageous (such as causing higher vaccine rates, increased physical distancing, fewer school children, etc.). States with a higher proportion of elderly may be more cognizant of risks – not only to the elderly themselves, but those around them, such as grandparents or elderly neighbors. Older states may have increased awareness or access to vaccines for the general population due to prioritizing vaccinations for the large elderly population.
But no matter the cause, when you compare Florida to its elderly peers (such as Maine, Vermont, West Virginia, etc.), it has underperformed.
What Went Wrong in Florida?
It’s important to remember which factors are associated with death rates: Education, healthcare, poverty, etc. Despite the Florida Man meme and jokes about rednecks wrestling alligators, meth, and general trashiness, Florida is a nice and relatively average state in many metrics that would determine public health outcomes. Yes, there is some Alabama mixed in here or there, but it is almost kind of approaching being a first-world place. It is also one of the most unique state (as seen in the dendrogram which shows clusters of similar states based on the characteristics used in this analysis).
It doesn’t have the worst high school graduation rates (-0.6 standard deviations from average) and its per capita incarceration is a little high, but nothing outrageous (0.7). It’s only slightly below average for population with a bachelor’s degree (-0.3) but it is above average for ‘white collar’ jobs (0.6). It has below average age-adjusted mortality: heart disease (-0.8), cancer (-0.7), respiratory (-0.6), and Alzheimer’s (-1.4), as well as obesity (-0.6). Overall, it has some positive things going for it despite some problems. It’s a purplish state that has plenty of normal, center/center-left people to counteract the fascists. By all accounts, Florida should have been below average – but not one of the worst. It certainly was not expected to be comparable to its Gulf Coast neighbors with their considerably worse quality of life indices.
We’ll never be able to definitively conclude the exact reasons Florida became one of the worst states, but there are some obvious guesses. Whether it was anti-mask hysteria, a lackadaisical vaccination strategy, or dangerous rhetoric from ‘leaders’ about the nonexistent risk to the non-elderly, the cumulative effects are clear: Florida has had one of the worst results after adjusting for its characteristics (and one of the worst overall with no adjustments). Whether this result holds through another winter surge is unknown, but in no way could Florida be considered a success story or a model to emulate.
Although the COVID-19 situation in Florida has dramatically improved over the past month or so – despite still being among the worst states in terms hospitalization rates (44th as of September 21) – the toll of the deadly summer wave will not be exactly known for many more weeks after all the data has been processed and reported. Although the picture is far from complete, there is enough information to give fairly accurate estimates of how many people have died. Several methods of estimation will be used and combined to give a rough, final estimate.
1. Reported Deaths
First, let’s start by using reported deaths. As you may recall, there is a renewed ‘controversy’ surrounding Florida’s death reporting. In Figure 1, we can see that deaths reported and deaths by date of death are highly correlated, generally tracking very closely over time. In the two previous waves, the curves of reported deaths have had lower peeks with wider spread than actual deaths, though they have a similar shape. Although not quite symmetric, we can use the wave’s peak as an estimate of its entire shape. Assuming the death reporting process has not changed dramatically for the most recent wave, it will likely be similar to previous ones.
In Figure 2, we see the 7-day average of reported deaths (along with a smooth line) with delineations showing each of three large waves (‘1’ being Summer 2020, ‘2’ being Winter 2020, and ‘3’ being Summer 2021). The delineations were generally determined by the derivative of the smoothed 7-day average and when the curve returned to the pre-wave level. That is, a wave begins when the rate of change in deaths reported becomes positive, ending when the rate of change in deaths reported becomes negative (some small bumps within the larger wave were ignored). The period between waves 2 and 3 was not assigned to wave 2 because deaths had already returned to the ‘baseline’ level by late April. As can be seen in Figure 1, these time periods match almost exactly with the actual death curve.
For the previous waves 1 and 2, there were 14,306 and 17,359 total deaths reported, respectively. In wave 3, there have already been 13,483 deaths reported with no definitive peak. Naively assuming a symmetric curve that is currently at its peak, there could be an additional 13,000 deaths reported for this wave, putting the cumulative total at more than 66,000 deaths. This could even be an optimistic estimate as the curve of deaths reported is likely skewed.
The percent of total deaths within the wave at peak reporting (i.e. cumulative deaths reported less than equal to the curve’s mode) was 34% and 48% for waves 1 and 2, respectively. It is conceivable that Summer 2021, which dwarves the wave in Summer 2020, could have caused even greater backlogs in reporting, making a curve that extends even further out. Let’s assume that wave 3 is currently at its peak and that deaths reported thus far are an average of the previous waves’ peak to total ratio, or 41%. This would result in a total of 32,900 (or 13,483/0.41) deaths for wave 3, of which 13,483 have been reported, making an additional 19,500 deaths and over 72,000 overall.
These estimates make plenty of assumptions and, admittedly, draw conclusions from just two ‘observations’ – the previous waves. However, if the current curve in Florida follows nearly every jurisdiction’s pattern in death reporting, this wave of reported deaths is, at best, only about halfway through – and it’s a long way down.
2. Hospital-Reported Deaths Only
As you may know, Florida hospitals report covid deaths to HHS daily (you can download the JSON file directly here). Not only is this a very strong predictor of the overall deaths on a given day, but it can also be used as a minimum value of the total deaths (although there are a handful of singular days where hospital-reported deaths exceed total deaths reported).
In this method, we simply take the difference between the number of deaths that hospitals have reported and the total deaths that have been reported. This can be visualized in Figure 3 with the 7-day average of each. Obviously, the exact number of hospital deaths is only known through the current day (as of September 23 for this writing) so some extrapolation is required to complete the curve. All that was done (which may be conservative) is to reverse the prior, increasing component beginning at the day where hospital deaths are less than the most recent known day. The extrapolation extends for six weeks (42 days) and ends at the baselines, pre-wave level.
Using this method, the minimum estimate of additional deaths is about 3,150, for a total of over 56,000 deaths. This could be considered an absolute minimum, 99.999% level of confidence – despite some guesswork over the next six weeks. Not only is the extrapolated portion likely an underestimate, it also assumes no covid deaths outside of hospitals which has never happened (when averaged over 7 days, removing individual daily anomalies mentioned previously). In fact, the ratio of total deaths to hospital deaths is rather large, at about 1.4. For the most recent 50-day period (6/20/2021 to 8/9/2021) of ‘complete’ death reporting (so, excluding the most recent 45 days when death reporting is assumed to be incomplete), daily total deaths reported were 40% greater than hospital deaths. It is possible that this 40% figure will decrease, but extremely unlikely to approach anywhere near zero.
There is also the possibility that it may exceed this 40% estimate if the threshold for hospitalization increased due to limited hospital capacity. Remember, Florida led the country in hospitalization rates (the most accurate non-hospital death predictor of total deaths) for much of the summer, with rates higher than at any point in the pandemic for any state since the New York/New Jersey initial wave in Spring 2020. It would not be surprising for more severe cases to go without inpatient-level of care that may have been admitted in previous waves.
3. Hospital Deaths Model
Recognizing that the number of hospital-reported deaths is highly correlated with the total deaths, why not build a simple statistical model? A general linear model with Poisson distribution using data from November 20, 2020 through August 9, 2021 (which excludes the past 45 days) was fit. In addition to the 7-day average of hospital deaths, the rate of change of 7-day average hospital deaths along with the 7-day average of proportion of total population fully vaccinated were also included. The fitted model is:
Total Deaths =exp[1.16 + 0.98 (log(7-Day Average Hospital Deaths))– 0.55 (Rate of Change 7-Day Average Hospital Deaths) – 0.27 (7-Day Average Proportion Fully Vaccinated)]
For example, the peak 7-day average of hospital deaths was 288 on 9/3/2021, with the proportion of fully vaccinated of 0.5406 and rate of change of hospital deaths of 1.003 (found by taking average on 9/3 divided by average on 9/2). Plugging these values into the equation gives a value of 400 expected total deaths on 9/3/2021 (for reference, only 303 deaths have been reported for 9/3 as of 9/23). Using this model, September 3 is expected to be the peak of deaths at about 400. For dates beyond September 23, the linear trend of the last seven days of predicted total deaths was extrapolated out 42 days (values were truncated at 20, simulating a return to the pre-wave level of average deaths).
As in the previous section, we can take the difference between what is expected and already reported (the difference between the red and black lines in Figure 4). Using this method yields an estimated 8,100 additional deaths to occur in this wave, making the total about 61,200 by November (not necessarily reported by then, but having occurred).
4. Method Averages
This table shows each of the methods discussed, some with variations. The range of estimates for total deaths from all methods is 56,251 to 72,588 with an average of about 62,800, or 9700 additional deaths to be reported.
Hospital Deaths Only (Section 2)
Hospital Deaths with 1.2 Total to Hospital Deaths Ratio (Section 2)
Hospital Deaths Model (Section 3)
Reported Deaths Curve with 50% Reported (Section 1)
Reported Deaths Curve with 40% Reported (Section 1)
Last year, when states and the federal government both failed to set up any reporting infrastructure about COVID-19 cases in schools in the United States, I teamed up with FinMango and Google to do it ourselves.
The Covid Monitor became the national tracking database for COVID-19 nation-wide in schools quickly, with valuable collaborations with groups like Johns Hopkins and The Washington Post to deliver data to parents and communities that was being kept from them by leaders across the country.
Our project put pressure on districts and states to start reporting, and before the end of the 2020-2021 school year, most states reported some kind of data about cases in their schools.
Here in Florida, concerned parents and school staff made so much noise about the state’s failure to report accurate data about COVID-19 in classrooms that the Public Health Accreditation Board, which certifies public health agencies in the United States, threatened to strip the Florida Department of Health for their failure to report schools data accurately, transparently and in a timely manner.
The findings represented not just a major victory for my own fight for data access and transparency, but for parents and school personnel statewide.
But the state decided to stop reporting cases in schools weeks before school actually ended in Florida, triggering yet another investigation into DOH’s practices that remains ongoing. With the final report – when corrected for the under-reporting of staff that was consistent throughout the year – Florida recorded more than 125,000 student and staff cases. That doesn’t include those who caught it at home from someone who caught it from school, and those that caught it from them, and so on.
The costs were nearly immeasurable. Studies across the countries decried the reopening of schools across the country during the pandemic, finding that more than 43,000 people contracted the virus and nearly 1,000 people died from it in Texas that would not have had schools remained virtual or proper precautions taken. The per-capita rate of infection in schools in Texas was much lower than Florida’s, though the state prohibited researchers from accessing the data to do a similar study.
Now with the Governor banning masks and vaccination requirements ion schools, even though multiple vaccines are required for public school attendance outside of COVID-19, and variants that make children sicker than before are spreading like wildfire throughout the state, the outlook for schools seems much grimmer than before.
I’ve published a map and data set for all data reported by the state for last school year. Please keep in mind that the state under-reported staff cases by more than 5,000 compared to data provided independently from the school districts themselves.
You can use the map or the table to find any school in the state and see how many cases were reported in each school from the end of September 2020 – mid-May of 2021. DOH did not included cases from August 2020 or late May/June 2021, and did not provide a justification for omitting those figures.
The CDC reported 16,868 new cases of COVID-19 in Florida yesterday, and another 16,337 cases today, as well as 252 deaths between the two dates.
The highest single-day case count last summer was 15,166 on July 12, 2020, meaning Florida just broke its own record for a single-day increase in cases during the summer/spring/fall months.
July 21, 2021 is now the 20th highest single-day increase in cases since the pandemic started last year – the other 19 days were all in December/January 2020. July 22 is now the 24th highest single-day case increase.
12,018 Florida residents lost their lives just in the summer months after reopening last year – with the highest number of deaths occurring just two weeks after peak cases that summer.
The only difference is that now we have no idea what’s going on. Last month, Desantis forced the Department of Health to stop updating all of the data feeds I set up for them in March of 2020. No more nursing homes data, schools data, prisons data, detailed demographic data, zip code data, community data, nadda.
And while Ron Desantis started selling merchandise with “Don’t Fauci Florida” slogans, and and Matt Gaetz proposed bills to fire the nation’s leading infectious disease expert, cases began rising in Florida- in large part to low vaccination rates, no mask mandates, and variants dominating the state’s new cases.
CDC variants data for Florida*:
P.1(Gamma) = 18.1%
*Data represents sequencing of new case samples from July 10-16, 2021, which is the most recent report available by the CDC. Click here for source.
A recent piece shared by my friend Dr. Peter Hotez shared issued dire warnings about transmissibility, severity and vaccine-evasiveness of variants. The author details the crisis in Florida and how this entirely-predictable disaster unfolded. (FYI, Dr. Hotez is world-renowned pediatrician and vaccine researcher that both Desantis and his Nazi-spokesperson have both publicly attacked for warning folks about this exact thing).
Here are a few key points about variants and vaccinations:
A Singapore study that is being submitted for international publication showed the following results for Pfizer and Moderna:
69 percent effective against infection regardless of symptoms
80 and 90 percent effective against symptomatic disease
93 percent effective against severe COVID-19 disease which includes requiring oxygen supplementation, ICU care, or death.
Being fully vaccinated provides high levels of immune protection although lower than previous strains (e.g., Pfizer: 93% against Alpha, 88% against Delta) and much lower if partially vaccinated (e.g., AstraZeneca only 33% against Delta)
The Johnson & Johnson (J&J) vaccine continues to underperform compared to its mRNA counterparts.
Another study found results consistent with the previous study mentioned, but also looked at AstraZenaca, which is more similar to the J&J vaccine, for the period from 5 April to 16 May:
2 doses of the AstraZeneca vaccine were 60% effective against symptomatic disease from the B.1.617.2 variant compared to 66% effectiveness against the B.1.1.7 variant
both vaccines were 33% effective against symptomatic disease from B.1.617.2, 3 weeks after the first dose compared to around 50% effectiveness against the B.1.1.7 variant
The big message: Get vaccinated. Soon. Both shots. Please.
We need to remember that children under 12 years old still are not eligible for vaccination – that includes my 11-year-old son, Jack, and my three-year-old daughter, Evi.
Hospitalizations of children are increasing with the Delta and Gamma variants, which are particularly good at infection and sickening children.
I still update the modern and classic versions of my website with the available CDC data each day. The sites have always been and will always continue to be free to the public, including all underlying data and archives. I made a promise to see this through, so that’s exactly what I’m going to do!
Florida has been among the worst states in the nation by case, positivity, hospitalization and death rates since last summer. Once President Biden took over and hired Andy Slavitt to run his data team, the White House began issuing weekly summary reports that showed rates of transmission and even ranked each state from best (#1) to worst (#50/52) for each reporting period.
For the last six months, Florida has averaged (based on rates per 100k):
#45 for cases #46 for positivity #44 for hospitalizations #44 for deaths
Florida’s National Standing in COVID-19 Response:
I personally don’t believe we should be discussing economics when we’re talking about people’s lives and safety, but since that is an argument that Desantis and his corrupt administration have tried to make, let’s look at the economics for a second.
Florida Pandemic Economics:
Florida ranks 3rd highest in percentage of children without enough to eat (12%);
4th highest in USA for percent of adults without enough to eat (11%)
3rd highest percent of population still behind in rent (21%)
11th highest percent of households reporting difficulty in “covering usual household expenses” (28%)
Our country should be making decisions based on sound science, not political posturing. I hope this information is useful and that you make safe decisions with the data that is out there, even if it’s now incredibly limited because of Desantis.
Congressional candidate Rebekah Jones (FL-1) seeks to have her @GeoRebekah Twitter account restored at once.
Jones recently lost access to her @GeoRebekah twitter account, where the whistleblower and scientist maintained an active following of more than 400,000 users at the time of her unjustified suspension.
According to an article in Forbes, Jones’ following showed consistent and organic growth since she blew the whistle on the state for lying to the public during COVID-19 last year. Only within the last few months – during the same period of time that Pushaw led an online smear and disinformation campaign – did any suspicious activity in user growth appear.
While there is no way to definitively prove who made the purchases or from where, Pushaw’s continued aggression toward this whistleblower goes beyond just unethical and immoral – it’s a violation of Florida law.
Even though Twitter sent Jones notice that her account was suspended due to violations of the spam policy, for allegedly over-zealously sharing a news article by the Miami Herald that exposed her former employers and the Governor, the Governor’s office began immediately lying to the public about the cause of the suspension in yet another failed attempt to smear Jones.
The governor’s office did this in direct violation of Florida Statutes 112.3187, known as “the Whistleblower law,” which protects those who file complaints from any adverse action or retaliation by the state.
Whether whistleblowers, congressional candidates, or both, an open forum for free speech should be encouraged when that speech is informative and factual. When critics of the state can be locked out of social media for nothing other than sharing news articles, however prolifically the sharing may be, Twitter effectively silences voices of dissent on behalf of governments.
We ask only that Twitter’s policies being enforced uniformly. Jones has been the subject of targeted stalking, harassment and smear campaigns with fewer than a dozen accounts suspended for their hateful behavior. Yet, Twitter expects the public to believe it suspended the account of a powerful governor’s most prominent critic for “spam.”
Please Tweet to or message @Twitter, @TwitterSupport@TwitterComms and advocate for Jones to have her account – and thus, her direct line with the people – restored and protected during ongoing lawsuits against the state and any campaigns she may manage in the future.
First, I headed back down to the Gulf Coast with a crew of documentary filmmakers to retrace the lifetime of hardship that made me into who I am today.
I visited my impoverished home town, the room where I sheltered during Katrina, the concrete slab where my parents’ house used to be before the Easter 2020 tornado destroyed it, the homes of my neighbors who died during that tornado, the bayou I spent years working on, and the tribe I spent even longer researching with, and the drowning city my heart breaks each time I leave.
During this trip, I received some welcome and unexpected news: The Florida Department of Health Office of Inspector General, after five months of investigation, found probable cause that what I said was true and worth further investigation, granting me – finally – protected Whistleblower status.
Then yesterday, exactly one week later, the Miami-Herald published the most detailed, well-researched and thorough story about what I witnessed at the Florida Department of Health from April-May, 2020 to date. You can read the story here.
The Herald’s story laid bare all that happened last spring, and will be the definitive history of how I ended up here, with this website and with all of you. The story thoroughly debunked conspiracy theorists peddling defamatory and dangerous disinformation once and for all. And although the dedicated work of Herald staff revealed the Desantis Administration for who they truly are, rather than apologize they dug in.
The week prior, the state announced it would be switching to weekly reporting following the week after Memorial Day. All data, reports, systems would be updated each Friday from then on.
Not surprising, given the state’s continued efforts to downplay the severity of the virus. Not surprising given Florida ranked #52 in the United States (+DC and PR) for new COVID-19 deaths per 100,000 people last week, per the CDC report shown below. Not surprising how other states, like Alabama, had made similar moves in recent weeks to cut down on resources allocated to the still-going pandemic.
What came as a surprise was the announcement yesterday evening – after the Miami Herald was released – that the state would discontinue all of its public data feeds, no longer update the state’s dashboard, and no longer provide any data except a nine-page summary report via PDF.
Grief. It felt like grieving to know they destroyed the data systems during a time when Florida was the worst-performing state in the nation in handling the pandemic. Grief to watch the state deny researchers, analysts, the press and the public any valuable information about what was happening in their communities. Grief knowing they likely did this to spite me.
The state stopped providing data to Johns Hopkins last week, so that is no longer a resource for this information. What data is available as an alternative to what we’ve lost is listed and linked to below, but if you are aware of other resources from which to pull this data, please reach out to me at: Contact@FloridaCovidAction.com
I will be spending the next few days working to find a way to restructure my website to loop in as much federal data as possible. Please be patient as the website is being worked on.
I’ve included a summary of the data we’ve lost, though the I’ll update this once I go through and make a combined list of all data fields now deleted from public access and the implications of having each of those variables out of public view.
What data we lost:
The heart of all data generated for the public has always been the case-line data reports, which provided the county, age, sex, case date, event date, etc., for each positive case confirmed by testing in the state. This data allowed for demographic, temporal and age-stratified analysis of every person testing positive in Florida.
It is gone.
SUBSTITUTE: None. CDC’s case line data lags by weeks, sometimes months, is often incomplete, and never timely. We’ll use it once it passes the data previously provided by DOH, but there is no true replacement for this data and its removal is a grave injustice to the people of Florida.
Thought not provided by DOH, the Agency for Healthcare Administration (AHCA) which had been reporting active COVID-19 hospitalizations, ICU utilization, and staffing resources/available beds appears to be down. No justification was provided for why this data is now gone, as its usefulness extends far beyond just COVID-19 surveillance.
SUBSTITUTE: Department of Health and Human Services open data (assuming DOH/AHCA continues to provide this data). Link here.
Vaccine progress and reports:
The more-thorough information about vaccinations by county has been removed entirely and reduced to a single snap-shot spread over two pages; though vaccine data was never provided through data feeds or public access to begin with – only ever through PDFs.
SUBSTITUTE: CDC COVID-19 vaccination data. Link here.
Pediatric reports, including MIS-C and pediatric deaths:
There is currently no way to know how many children under the age of 18 have died because of COVID-19 in the state of Florida. DOH has now decided to report pediatric deaths – which are all persons under 18 – as only those under 16, so 16 and 17-year old pediatric deaths are no longer discernable in the data.
There were three deaths of 17-year-olds and two deaths of 16-year olds – that represents nearly half of all pediatric deaths that are now hidden.
ALL Emergency Room surveillance data:
The hospital surveillance data that was supposed to determine the relative safety to reopen – the COVID-19-like-illness and influenza-like-illness ER reports that tend to be a precursor to case, hospitalization and death data – are all gone.
City and zip-code data:
The state never provided city data as an API, but I scraped it from PDFs and built APIs to provide it myself. Since the state will no longer be issuing those reports, there is no way to update the data for cities from here on.
The zip code data will also no longer be updated.
SUBSTITUTE: None. These two data feeds (mine and DOH’s) were the only sources for local data provided by the state or federal government.
Although most districts in the state will remain open through next week, The Florida Department of Health removed all school reports from their main website. It is not clear when or if they will update this data for the rest of the school year.
SUBSTITUTE: None. There is no federal tracking resource for COVID-19 cases in K-12 schools. We decommissioned The Covid Monitor in March on the basis that the federal government would overtake this task. They have not. There is nowhere else to get this data.
ALL non-resident data:
The state has finally pulled the plug on reporting any and all non-resident cases, vaccinations, and deaths. If you do not have a valid Florida proof-of-address, whether you’re a person who spends half the year in the state or a migrant who lives her on a visa, a visitor staying long-term, or any other situation in which you do not have permanent, legal Florida address, you are deleted from the public data entirely.
All long-term care facility data, including cases and deaths
All data about nursing home cases and deaths has been removed and will no longer be provided.
All that is left for data about cases and deaths in prisons is now listed on the Department of Corrections’ website here.
What to be careful using in the new format:
Positivity vs “new case” positivity:
A little tricky because of how it’s reported, but the state is now providing a metric for “cumulative case positivity” and “previous week new case positivity.”
“New case positivity” is the calculation DOH invented for the sole purpose of artificially deflating positivity by including duplicates in the denominator for all persons tested, but only counting new cases (first-time positive) result in the nominator. This statistic is used only by the state of Florida – no other state or country in the world uses it. There’s a lot more on that here.
Cumulative positivity – which you’ll notice ranges from 7.5% to 28.1%- is much higher than “previous week new case positivity.” So putting these two statistics next to each other as if they’re indicating a dramatic decrease in comparative figures is incredibly dishonest and misleading. They’re not measuring the same thing.
Still, the cumulative positivity by race and ethnicity is telling – 28.1% of White-Hispanic persons tested since March 2020 were positive. And vaccination rates for White-Hispanic persons is inexcusably lower than White-Non-Hispanic (26% to 33%, respectively).
This page will be updated as more information is gathered. I wanted to get something out quickly as I know many of you are likely wondering what happened and why all the data is down.
If you’re looking to help out, whether to send a check to help my ongoing fight against the state or just to send a note to say hi, you can now write me at:
Submit your nominee(s) for COVID Action’s “Data Hero” awards, which recognize the hard-working folks in the public, private, and non-profit sectors who have kept you informed about Coronavirus in your state or region.
COVID Action is looking to identify and support the scientists, reporters, and community members who provided transparent and meaningful data and analysis about COVID-19 cases, hospitalizations, deaths, testing, vaccinations, and more, to the public during the last year.
Nominees may include any individual, including:
Local, state and federal employees
Reporters and members of the media
Elected officials, community members, candidates
Non-profit and volunteer researchers
Scientists, statisticians, geographers, epidemiologists, or anyone with an academic or professional background related to their work, regardless of current employment/affiliation
Please provide as much information as possible for your nominee.
The list of data heroes will be published on our website at FloridaCovidAction.com on March 1. We’ll also publish a map with profiles of some of the most outstanding heroes across the country with links to the resources, data and analysis they provide(d) by March 12 – one year from the day our founder published her internationally-praised Florida COVID-19 data and surveillance dashboard for the state of Florida.
Our team will review all submissions and identify three individuals among the pack who led data reporting and analysis during COVID-19 under immense pressure with transparency, accuracy, and incredible talent and grit, particularly in the face of opposition or interference. Those winners will be announced on March 12 with the launch of the Data Heroes Map.
Each of the three individuals will receive a $1,000 award for their efforts in keeping the public safe and informed.
I thought a lot about how to make this announcement. In true “Rebekah Jones fashion,” I’ll just come out and say it:
I will be leaving Florida next month.
After the raid on my home, I can no longer justify staying in the state.
I am not abandoning Florida. I am not abandoning this project. I will continue to do this work after I move until it is no longer needed, as I’ve always promised.
The governor of Florida seems have an irrational and passionate hatred for who I am and what I represent: defiance in the name of science of human decency.
He crossed a line involving my family. The depravity of having his personal police force storm my house and point guns at my young children cannot be overstated.
That action may very well become a life-changing moment for my son, who at only 11 years old had multiple police officers pointing guns at him.
We were kept in our living room for hours while police went through our things – an armed babysitter not allowing us to leave the room.
My family is no longer safe living in this state, and I cannot and will not ask them to make sacrifices of that magnitude for the work I am doing.
My team of amazing lawyers are working to hold the police and the governor accountable for these actions.
Despite the raid on my home and my family, my greatest concern is not about police intimidating whistleblowers.
Science has been at war with politics in this country since its founding. We’re barked at for getting pulled into the fight, but if we can not be advocates for our own work, then we can not sit back and expect other people to become advocates in our absence.
I saw first-hand how information is manipulated for the advancement of an agenda completely departed from the facts. I refused to be a part of it.
It reminds be a lot of the history of science related to smoking and cigarettes.
There have been many great books written about this topic, but if you’re able, get a copy of Merchants of Doubt, by Oreskes and Conway.
I wrote a short piece below about that fight, and I hope you take the time to read it. It exemplifies the struggle of scientists against political forces.
Franz Herman Muller, Eberhard Chairer, Eric Schoniger, Ernst Wynder, Evarts Graham, Rochard Doll, Bradford Hill, Raymond Pearl, Alton Ochsner, Charles Cameron and others were among the early researchers of the tobacco-cancer link. Their names are not well-known, but they put into motion all of the research that ultimately saved millions and millions of lives from cancer-related illness.
Alton Ochsner, another researcher looking at the cancer-linked, was stabbed in the back by his own colleagues for being “an anti-smoking enthusiast,” as if being an advocate of his research findings somehow made those findings illegitimate.
Cigarette companies had their own medical researchers who claimed smoking was great for your health – literally. Even Harvard University speculated as to why the research might be wrong.
Influential doctors like James Walsh made outrageous statements about how they knew healthy people who smoked and that the entire medical field had no reason to spend its vast resources researching the emerging science on the smoking-cancer link.
In 1952, emerging research on the link between cancer and smoking broke through the academic-public barrier after a widely-shared article in Reader’s Digest was published. There was a sharp decline in cigarette sales, but it was temporary, and cigarette companies began stepping up their marketing and lobbying to protect sales.
In 1955, Dr. Charles Cameron, medical and scientific director of the American Cancer Society, published a best-selling book detailing the connections between smoking and cancer, and offering preventative measures. For his efforts, he and other leaders in medical research were harassed, threatened and even fired from their positions.
Cigarette-funded “scientists” called into question the quality of data being used. They said they had never met someone who had died from cancer due to smoking. They dismissed researchers as being “fear mongers.” Sound familiar?
Here’s a great excerpt from The Atlantic about how politics interfered with the science:
“Politics also played an important role. In “The Quiet Victory of the Tobacco Lobby: How It Found the Best Filter Yet—Congress” (September 1965), Elizabeth Drew explained how business people with vested interests in the tobacco industry were exploiting their high-level political connections to influence tobacco-related government policies. And in a 1992 article, France: An Ambivalent War Against Smoking,” Judson Gooding discussed how similar factors were then operating in France to stymie anti-smoking legislation. In this case, he explained, the vested interests resided not with outside lobbyists, but, paradoxically, within the government itself, which owned the nation’s cigarette-manufacturing monopoly and reaped substantial tax revenues from cigarettes.”
An article published in 2011 actually calculated the profit made for cigarette companies per each person who is killed from their products: $10,000.
A cigarette company profits $10,000 for each person they kill.
Today, we’d view anyone who denies the link between cancer and smoking as a moron, and smoking is generally frowned upon (as it should be). But it took decades of fighting the cigarette lobby and congress to get that information out to the public, and then turn it into legislation.
The same people who led the misinformation campaigns about smoking and cancer would later lead the disinformation campaigns against climate change science, using the same tactics to undermine the global body of research showing the definitive link between man and global warming.
When I decided to launch my Florida COVID Action site on June 11, I had no idea if anyone would care or even notice.
No state in the country had been so plagued by corruption and politics in data reporting as to warrant an “alternative” resource of official information.
But when I was fired as manager of the public data and surveillance data systems at the Florida Department of Health in May, the media attention blew up beyond what I ever imagined.
In the process, an information void was created. Knowing the data the state was pushing was unreliable and untrustworthy, Floridians had nowhere to turn for information about COVID-19 in their state. I felt somewhat responsible for that void and thus felt morally obligated to try to help those who didn’t trust the state.
So I committed myself to continue doing the job of reporting the data and science to the public, free of charge, for as long as they needed it, pulling in all available state datasets to give the people as much information as I could scrape.
I have been living and breathing this project night and day since that time, and the support I’ve received both personally and for my new ‘company’ has been tremendous.
Less than two months later, I watched another problem develop that no state in this country had yet faced: the potential impacts of reopening schools during a pandemic.
Most states closed schools for the year in March, when the number of cases across the entire country was only at about 7,000 total.
Teachers started reaching out to me directly with their concerns about the upcoming school year. Concerned about retaliation, they asked if there was a way they could report what was going in schools without being identified.
So I launched a new dashboard to provide teachers, staff, administrators, parents, and even students a way to safely and 100% anonymously report information about cases in schools, even if officials didn’t want to release the information.
I immediately became swamped with reports – not just in Florida, but across the entire country. Hundreds a day. Working on this site and the new schools project became a 20-hours-per-day job. I was exhausted. I was worried that I would neglect either or both projects if I continued to try to handle that work by myself.
So I started reaching out for help. I wrote letters to universities, and academic and professional researchers about my idea for the project: a central database tracking and reporting cases in every school in the country. I even wrote on Twitter that I needed help.
Enter FinMango,a financial literacy non-profit (501c(3)) who shifted gears to COVID-19 education during the pandemic. They had partnered with Google’s COVID-19 Open Data Project, and had the passion, intensity and resources needed for a successful collaboration.
If you’re a school administrator or state official and would like to connect with us, please email us at: Plans@TheCovidMonitor.com
Members of the media are encouraged to participate in our data collection system, as well. To learn more about how to automate your data reporting with The Covid Monitor, please contact plans@TheCovidMonitor.com
FinMango is a global non-profit organization on a mission to promote and advance financial inclusion worldwide—however, we recognize the importance of combating COVID-19 and are actively pivoting towards catering to the needs of researchers leading the fight.
Florida COVID Action LLC is a Florida-based social benefits corporation made of scientists specializing in public health research, data science, and data visualization. Founded by Florida scientist and whistleblower Rebekah Jones, Covid Action is committed to data access and transparency during the coronavirus pandemic (and beyond).
FinMango and Florida COVID Action are partnering on a ground-breaking joint project, launching the nation’s first COVID-19 dataset focusing on coronavirus cases in every K-12 school district in the U.S.A.
Our two organizations utilize and connect with a wide range of data providers to collect information pertaining to COVID-19, including school opening dates and formats, restrictions and policies regarding masks and distancing, and the number of cases for each district.
Data currently available about COVID-19 cases in schools exists in fragmented pockets across the country with no standardized reporting method in place, making it difficult to locate, synthesize and analyze.
Our initiative ensures that COVID-19 data is accurate, transparent, and published in a single, public platform—an effort that will more effectively inform decision makers as they work to understand and mitigate the effects of COVID-19.
Through this partnership, FinMango is expanding its work to address the pandemic, with the use of Google Cloud’s COVID-19 Public Dataset Program, a hosted repository to aid researchers, data scientists, and analysts in the effort to combat COVID-19.
For more information or to volunteer, visit us at finmago.org/covid or contact us at email@example.com.
Information about our collaboration team:
FinMango is a global non-profit organization on a mission to promote and advance financial inclusion worldwide—however, we recognize the importance of combating COVID-19 and are actively pivoting towards catering to the needs of researchers leading the fight.
Florida COVID Action LLC is a Florida-based social benefits corporation made of scientists specializing in public health research, data science, and data visualization. Founded by Florida scientist and whistleblower Rebekah Jones, Covid Action is committed to data access and transparency during the coronavirus pandemic (and beyond).
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In a post at the end of 2021, we explained how deaths would likely increase substantially in the first weeks of January. On January 1, a few days after being published, the reported 7-day average of deaths was less than 20 per day. By January 23, a little more than three weeks later, weekly reported deaths had increased to 130 per day. While DeathSantis’ Ministry of Propaganda assured everyone that everything was fine and there would be only a ‘mild’ increase in deaths (if they would even admit that), anybody with half a brain knew this was just another lie – especially laughable given the record-breaking number of cases and rapidly increasing hospital admissions.
Sure, it is satisfying to be proven right (in a perverse way, given the circumstances), but the truth is that this was a very unremarkable prediction as hospitalizations, the best predictor of the death toll, were increasing at the time. Everyone that isn’t a total political hack devoted to spreading the cancerous rot that is Ron DeSantis said this was going to happen – you know, because science and math and stuff.
Now the question of course is, “how high will deaths go?”
In our previous post, we looked at differences between predicting hospital deaths from ICU numbers and overall hospital census. For the Summer 2021 wave, overall hospitalizations were a more accurate predictor of hospital deaths than ICU numbers. Unfortunately, as of January 6, HHS is no longer requiring hospitals to report deaths due to COVID in hospitals, leading to a substantial drop in the number of Florida hospitals reporting this invaluable data. Our prediction that the current wave’s hospital deaths would fit better with overall census rather than ICU census has thus been rendered inconclusive. This is a serious detriment to tracking the situation as hospital reported deaths were one of the few real time pieces of information available.
Nevertheless, we can still make meaningful observations with the remaining data. Recognizing the close relationship between deaths by report date and deaths by occurrence date (total deaths, not hospital reported ones), we can approximate how many people died several weeks ago.
As we see in the figure, the number of deaths occurring on a particular date is about the same as the number of deaths reported two weeks from that date. If 100 deaths are reported today, about the same number died two weeks ago. For more than a year, this relationship has been consistent in Florida, but there is obviously no guarantee that deaths will be processed in the same way in the future. Assuming Florida DOH continues to report deaths at the same rate, then it is safe to say that about 74 people were dying two weeks ago (January 10 at the time this was written). Due to the way Florida started to report deaths beginning in the summer of 2021 (twice a week, on Monday and Thursday typically), this produces a jagged 7-day average. This doesn’t matter too much as the extreme fluctuations average out but will produce more extreme residuals and overly variable day-to-day estimates. If reporting days are shifted due to holidays, there will be large artificial jumps in the 7-day average.
Note that the ‘Deaths Reported’ in the figure is not truly the number of deaths reported but fitted values from a statistical model that is nearly equivalent. The number of days lag from 0 to 21 was fit with 14 resulting in the minimum model deviance. The most recent 45 days were removed due to incomplete death data. Black dashed lines in the plot indicate the most recent 45 days of deaths that are expected to increase as reporting progresses. Red dashed lines indicate the predicted number of deaths for this incomplete period. Using this method, the number of people dying two weeks ago (January 10) is likely between 74 and 127 – call it 100.
In fact, the 7-day average of deaths on January 10 is already 74 (as of January 24) but this number will almost definitely increase substantially over the next month or more. The 74 and 127 estimates are from the most recent predictions from 14 and 15 days ago, respectively (the actual 7-day average of reported deaths is 78 and 130 for these days).
In this figure, we see the relationship between total deaths (by date they occurred) and total hospital census shifted seven days back. The turquoise dashed lines show 2, 4, 6 and six weeks ago, along with the two most recent days’ predictions from the first section (black dots). As we can see, total deaths are highly correlated with overall hospitalizations (excluding the most recent month or so while deaths are being added). The observed deaths in November and December is actually greater than would be expected given the number of hospitalizations.
Hospital census has seemingly peaked about a week ago, so overall deaths are probably peaking now (the 7-day lag of hospital census to deaths produces the best fit) while reported deaths will reach a maximum in about two weeks. This might extend another week due to the slower decline in hospitalization numbers (and there has even been a slight increase in cases this week, which may complicate things).
Going back to the ‘Census vs ICU’ question from the previous post, the next figure shows the predictions for the most recent five weeks based on data from the preceding 200 days (i.e., 242 to 43 days back). It is apparent that the ICU numbers (5-day lag) in Florida do not have the same relationship to deaths as in previous waves. That is, a greater number of people are dying per ‘COVID ICU bed’ now than previously. Using the overall hospitalization numbers, however, there is good agreement up until the past few weeks where deaths are notoriously underreported (Florida DOH is still adding/subtracting deaths from August 2021).
Using the much more accurate ‘Census Model,’ deaths are expected to peak at over 250 per day – much greater than the numbers being pushed by the ICU is Low/Omicron is Mild/For vs With crowd. Although ICU numbers will not reach half of the Summer 2021 wave when deaths peaked at about 400 per day, there will very likely be more than half the deaths. This would be a greater peak than both the Summer and Winter 2020 waves – neither of which had widespread vaccines or treatments available (or a state health department helmed by a whacko that can’t answer a yes/no question about whether vaccines are safe and effective).
The good news is that hospitalizations are decreasing and actual deaths occurring today are probably the highest they will be or even slightly past the peak. Unfortunately, there will be several more weeks of increasing number of deaths reported.
In a recent post, we wrote about how hospital numbers were expected to rise in the week before the new year due to the rapidly rising hospital admissions from the previous week; unsurprisingly, this has come to pass. Now, we can confidently say that the 7-day average hospital census should be about 7,000 by January 6 (as of December 30, it is just under 3,400 with a likely underestimate of today’s numbers as these are simply carried forward from yesterday’s census). In other words, the number of people hospitalized with COVID-19 in Florida will probably double in the next week, reaching 40% of the summer’s maximum hospitalization numbers with case numbers still increasing seemingly unabated.
We all know the screwy way Florida likes to report deaths in an effort to mislead the public. Fortunately, Florida hospitals report hospital COVID deaths daily and this is an excellent approximation to total deaths on a given day (see section 3 here). As a quick rule of thumb (at least up until this point), the total deaths on a given day is about 40% greater than those reported by hospitals. For example, if hospitals report 100 deaths, the overall statewide deaths are expected to be about 140. Of course, there are many unknowns with a new variant and opaque government processes, but we will assume that this relationship will remain true in this wave.
Given Florida state officials’ proclivity toward shameless self-promotion at the expense of its citizens, expect many embarrassing and juvenile tweets from state employees/paid trolls or favorable media coverage about how deaths have remained low despite record cases and rapidly increasing hospitalizations. But just as every DeSantis victory lap precedes a massive wave of death and suffering, this latest round of propaganda will be proven embarrassingly wrong in just a few weeks’ time.
Consider the following graph showing weekly averages of hospital census with hospital-reported deaths (shifted seven days back). Note that this is all inpatient beds with COVID, not just those in ICU. We can see the near-perfect relationship between the number hospitalized and those dying within the hospital seven days later (the correlation over the past 200 days is 0.998). Considering that hospitalizations have only just begun rapidly increasing the past week (and deaths lag another week), hospital deaths were not expected to rise very much – until now, that is.
As of December 30, the seven-day average of hospital deaths was 25. Following the rapid increase in census numbers beginning two weeks ago, hospital deaths are expected to nearly quadruple to about 100 deaths on January 13. Using the 40% estimate from before, this would be about 140 overall deaths by mid-January with the peek likely a month away (last winter’s wave peeked at 200 deaths/day). These death estimates rely not only on predictions from the hospitalizations to hospital deaths seven days away (see formula), but also those from hospital admissions to census seven days away (used here). To estimate the number of hospital deaths on a given day, use the following formula:
Hospital Deaths = exp(–6 + 1.2 x log(7-Day Average Census 7 Days Ago))
These estimates could be totally wrong, remember. The relationship between cases and hospitalizations seems significantly different than previous waves, but less is known about hospitalizations and deaths. Thus far, hospital reported deaths have increased as expected following an increase in hospitalizations. If past relationships continue, Florida will almost assuredly experience yet another wave with over 200 deaths/day.
Like waves of COVID (and toxic red tide) continually washing over Florida, so is the bullshit from the Florida Department of Health.
Since I wrote to you last October, not much has changed in the way the Florida state government has handled the pandemic: do nothing; declare premature victory; get fellated by conservative propaganda outlets (along with “centrists”) while cases decline for a few weeks; and then, ultimately, become a national embarrassment when things predictably blow up. I mean, who cares if tens of thousands of people die when have people like this guy lining up to be your fluffer for free? Who could resist that neck-beard-face?
But, once again, in times of crisis, it looks like the Florida Department Of [No]Health (DOH) has pulled out the same old playbook of gaslighting and obfuscation rather than doing the hard work to address the problem that is killing thousands of its citizens.
I won’t really address the controversy that was this past weekend’s data reporting debacle, as it could have been easily avoided by not relying on the federal government to do Florida’s job and simply go back to daily reporting. We all know they won’t because the official party line is ‘Covid is Over and Glorious Leader DeSantis Was Victorious,’ but DOH really came across foolish and unprofessional … again. Seeing how the discrepancy is a tiny percentage of the overall cases and more important metrics like hospitalizations are readily available, it’s probably not worth getting worked up over at this point.
(However, I wouldn’t be surprised for them to manufacture some scandal so that right-wing news could create conspiracies and mistrust of the CDC to distract from the unabated record-breaking hospitalization and soon-to-be death numbers.)
But the real scandal is in fact the death data and how it unexpectedly changed on the CDC Covid Tracker on August 10.
As we discussed last October, DOH is fond of highlighting the misleading ‘deaths by date of death’ chart to make things look much better than they are. Because there is a lag in the reporting of deaths, the current day always appear that deaths are decreasing – even in times of extreme increases. Deaths that occurred more recently take time to be logged so there is always an artificial decrease when deaths are plotted by the date that they occurred. Several weeks or months must pass before those numbers become accurate. Read this for more information on deaths reported vs occurred.
Before today, all data from the CDC had been by the date that deaths were reported. Deaths that are reported today occurred over a span of several weeks, not necessarily today. It appears that DOH has switched to deaths by date of death to try to hide the 244 additional deaths that were reported between August 9 and August 10 (along with the 239 were reported over the weekend) and obscure the fact that deaths are now rapidly increasing.
Rather than give the CDC the difference in deaths between today and yesterday like they had for months, they appear to be reporting by date of death now because it will always look like deaths are decreasing. This intentional change in how the data is reported is all about misleading the public and changing perception which seems to be the primary goal of the Florida government during the pandemic. In fact, some news outlets have already fallen for this misleading tactic and are reporting “new” deaths as ones that occurred yesterday despite that number being a small fraction of what it will eventually become.
I hope the CDC and the public sees through this latest manipulation of data by the FLDOH and calculates the true difference in reported deaths. Or, better still, put pressure on this corrupt agency and state government and demand they adhere to data reporting standards that seemingly every other state in the country adheres to.
I dream of a day when I get to bring people good news instead of doom and gloom. As a climate scientist and COVID-19 data expert, that day has yet to come for me and I’ve had the harrowing burden over my career of being a proverbial ‘data dark cloud.’ But I have faith this pandemic will end eventually and our country will heal and I can bring news of progress and good health to people.
Today is not that day.
The CDC released its weekly summary report for each state earlier this afternoon, showing Florida last or next to last in every single indicator, including new cases, hospitalizations and deaths.
Florida has fallen back into high transmission rates again, with cases increasing 58% over the previous week, hospitalizations up 54% and deaths up 60%.
The state did not submit data to the CDC as other states did from July 24-27, but rather provided data today that showed an increase of nearly 40,000 cases over the missing days, without including antigen testing data.
Florida has yet to break the top 50 for vaccination rate by state, sitting at #26 (it was stuck at #27 for the previous five weeks, that is some measure of progress).
The county with the lowest overall vaccination rate in the state is in our district – Holmes, with a shockingly low vaccination rate of 25%. The remaining district 1 counties – Escambia, Okaloosa, Santa Rosa, and Walton – range from 43-46% fully vaccinated – well below the national average.
What jumped out most about this week’s report were changes in the percent of variants among sequenced cases. The only place to get information about variant cases is the CDC. The state of Florida still refuses to publish it.
With school starting across the U.S. southeast in the coming weeks, there’s another number to be mindful of: vaccination rates for those 12-18 years old. The CDC provides some data about those age groups, however, and you can browse that data here.
As a percent of total vaccinated in the state, Florida ranks in bottom 10 for the share of all vaccinated who are children ages 12-17.
Florida law bans adolescents from receiving the COVID-19 vaccine without parental consent – a law that Florida officials could quickly overturn if they were serious about getting children vaccinated from the virus. Other states allow teenagers to make medical decisions for themselves – including vaccinations – mostly in the south, including Alabama, Arkansas, North Carolina, and South Carolina, but also the Pacific Northwest.
Then there’s the issue of waiting for emergency approval for the 6-12 cohort, and eventually younger than 6 cohort.
Children under 12 are not yet eligible for the vaccine. That includes my son, Jack (11), and my daughter, Evi (3). Every unvaccinated person poses a greater risk to the health and safety of not just my children, but of all children who are currently ineligible for the COVID-19 vaccine.
The anti-vax misinformation that has been propagated by FOX, OAN, Desantis, and the anti-vax network is killing people. It’s time to put a stop to it.
Ask your family, friends, neighbors if they’ve been vaccinated. If not, ask them if you can help them book an appointment, or offer to give a ride. This site makes it easy to find the next available vaccination appointment in your area.
There’s also the larger question of what the cost will be for the continued immoral leadership of those like Desantis who have peddled COVID-19 conspiracy theories throughout the last 18 months.
Desantis, Trump, and the failure that is the Florida Department of Health leadership are responsible for the current surge in cases, which will follow with hospitalizations and deaths. They again played games with people’s lives and have lost.
How much ground have we lost as a nation because Florida decided not to implement mask mandates, and even actively worked against them?
How much progress has been erased because of the botched vaccine rollout and lack of vaccine advocacy by the state?
How much damage is being done because the state continues to hide the data from the public, despite public pleas from activists, scientists and doctors?
DeSantis denied the reality of COVID-19 in Florida from the start.
Instead of following the science and putting people’s health and safety first, he has again recklessly thwarted all progress made with vaccinations by hiding the data from the public, overruling local laws, and dogmatically attacking scientists.
The people continue to pay the price for his cruel policies.
Worse now than before, though, is that no one outside his administration knows what hell is going on.
Where are these cases?
Nursing homes? Prisons? Service and hospitality industries?
We have no information. We’re blindfolded going into this surge, and Desantis just found out how dangerous keeping the public in the dark can be.
As a reminder, here is where Florida has stood every week so far this year: