Categories
Innovative Solutions Predictions

Predicting Hot Spots

Because COVID-19 has an average 5-day incubation period (ie humans can spread for 5 days before showing symptoms), it’s given epidemiologists quite the headache to stop spread before it happens. It’s been imperative and incredible to watch how epidemiologists have worked with other industries to predict future COVID19 hot spots.

Twitter and Google: Scientists found a way to predict COVID19 cases and deaths hotspots 2-3 WEEKS BEFORE they happen. How? Through Twitter and Google internet activity. If the number of tweets with the words related to COVID19 (like covid, corona, epidemic, flu, influenza, face mask, spread, virus, …) start increasing, we can predict cases 3 WEEKS in advanced and deaths 4 WEEKS in advance. They also found that by counting the number of people that searched for “fever” in google can predict COVID19 deaths 22 days prior to a spike.

Kogen et al. (2020). An Early Warning Approach to Monitor COVID-19 Activity
with Multiple Digital Traces in Near Real-Time.

Other innovative solutions? I posted previously on a few methods that predict hotspots 5-7 days BEFORE they arrive:

Fecal matter: In sewage systems. Epidemiologists in New York found that high levels of the virus in sewage can predict outbreaks 7 DAYS BEFORE the outbreak occurs. AND, not only outbreaks but can predict an increase in hospital admissions. In other words, if we test sewage systems, we can tell a week beforehand whether there will be an outbreak in that city. Most recently, this has been an effective method in airplanes and cruise ships too.

Symptom tracking: Some of my colleagues are using a symptoms to predict outbreaks 5 DAYS BEFORE they start. They are simply asking if people are well or sick through an app, and if they are sick asking what symptoms do they have. This strategy has been highly effective in the UK.

Google mobility data: I’ve posted phone data extensively in the past. But, most recently, we were able to predict the recent second “spike” in the southern states by seeing how people’s movement to non-essential businesses changed.

There are other ways I’ve heard, like credit card useage, but I haven’t seen published science on this yet.

Without effective drugs or vaccines, these are really useful strategies to deploy public health efforts BEFORE a hotspot hits. Can’t wait to hear what else scientists come up with!

Love, your local epidemiologist

Important Note: Figures come directly from the article. I did NOT create these.

Data Sources:
Google/Twitter article: https://arxiv.org/pdf/2007.00756.pdf…
Sewage article: https://www.medrxiv.org/…/10…/2020.05.19.20105999v1.full.pdf
Symptom tracking article: https://science.sciencemag.org/content/368/6497/1362.full…

Categories
Children Innovative Solutions

Opening Schools part 2

Alright, an update from yesterdays post…

First off, you are all impossible to please. BUT because of your fantastic feedback, we were able to make this table a bit more detailed. I added the ages of the grades that were opened and incidence of COVID19 (per 100,000). I also corresponded incidence rates to Harvard’s “Key Metrics for COVID19 Suppression” (see Figure).

All the countries that opened schools were either GREEN (below 1 case per 100,000) or YELLOW (1-9 cases per 100,000).

As for the United States….
I added a graph of the incidence rates per state. As of today (and according to Harvard’s standards), we have no GREEN states. However, about half of our states are YELLOW. The other states have high incidence rates: ORANGE or RED.

Each state, county, and city needs to pay attention to their incidence rate and plan accordingly. If a location is in the YELLOW or GREEN, I would also humbly suggest that they ALSO need to have their test positivity rates AT LEAST below 10% (5% would be ideal, according to the WHO).

Schools are not opening today. They are opening in a month or two. So we need to plan for ALL scenarios and be flexible. If we still have record breaking numbers in a month or two, we will have much bigger problems than opening up schools.

Love, your local epidemiologist

Sources: Harvard: https://globalepidemics.org/key-metrics-for-covid-suppress…/ . University of Washington, Global Health: https://globalhealth.washington.edu/…/COVID-19%20Schools%20…

Categories
Children Innovative Solutions

Opening Schools part 1

For the teachers. I know you’re out there because you’re so loud in the comments (keep it up).

There is VERY LITTLE data on the risk of COVID19 to teachers because…schools have been closed. School closure was one of the first mitigation strategies implemented because TYPICALLY kids (and elderly) are the worst hit during an epidemic. I have daycare data (but, again, only for kids).

Some rockstar researchers at the University of Washington spent the time to go through ALL national and international media reports AND studies to put together the most comprehensive picture of COVID19 spread in schools. They included all countries that have opened schools, what they have done, and whether or not they have seen COVID19 spread.

I threw together this figure so it’s easier to see on social media. HOWEVER, I strongly suggest to read the report. Yes, details are missing because there is a lot we just don’t know.

Without rigorous data, the only thing we, epidemiologists, can suggest is harm reduction: only open up certain grades; have staggering schedules; and control transmission. Also, keep in mind that all of these countries have different cultures, different family values, different school schedules, and opened at different points during the pandemic. All of this will impact spread. However, this is the best we got.

We have to be extremely strategic in opening schools. There are epidemiologists in every county across the United States. Work closely with them to navigate this unprecedented time.

Love, your local epidemiologist

Data source: Shout out to this team for their work in gathering this important information: Brandon L. Guthrie PhD, Diana M. Tordoff MPH, Julianne Meisner BVM&S MS, Lorenzo Tolentino BS, Wenwen Jiang MPH, Sherrilynne Fuller PhD FACMI, Dylan Green MPH, and Diana Louden MLib, Jennifer M. Ross MD MPH. Here is the report: https://globalhealth.washington.edu/…/COVID-19%20Schools%20…

Categories
AZ California FL Innovative Solutions Long-term effects New York Social Distancing

Cell Phone Tracking

Using cell phones to track movement.

Tracking the way in which humans moved before and during the pandemic has been a very innovative way in which epidemiologists have been able to describe (and predict) COVID19 spread. Specifically, many scientists are using cell phone data to track movement.

Yesterday, the Lancet (a highly reputable scientific journal) published a study in which they wanted to answer… HOW strong IS the relationship (i.e. correlation) between movement and COVID19 spread. Spoiler: VERY strong.

We can see this visually too. For example, as of today, there are 14 hot spot states. These states have very similar patterns in movement to non-essential businesses (Figures). The blue line indicates change in movement to non-essential buisnesses. For example…

In Texas, at the peak of the stay-at-home orders (April 8), there was a 70% reduction in movement to non-essential places. In other words, people moved 70% less to non-essential buissness than before the pandemic. Which was great; it worked to curve spread. However, since then, people have been moving more and more to non-essential businesses. In mid-June, Texans only moved 15% less than before the pandemic. This means they were almost back to “normal”. This was followed by exponential increase in COVID19 cases.

We see the same with AZ, FL, and CA (although CA is not as dramatic).

This is CA… forgot the label

As a comparison, I also included NY. Movement to non-essential businesses stayed constant for almost 2 months, then once cases were down, SLOWLY started to increase. The highest NY has gone is 55% reduction in movement. They haven’t even gotten close to the 15% reduction like we see in Texas.

Translation: Your movement to non-essential places MATTERS! We can all reduce our movement to keep this pandemic under control.

Love, your local epidemiologist

Sources:
Lancet study: https://www.thelancet.com/…/PIIS1473-3099(20)3055…/fulltext…

Mobility data and graphs: From UnaCast. A really fun site to play around with: https://www.unacast.com/covid…/social-distancing-scoreboard…

Categories
Innovative Solutions National changes Social Distancing

COVID19 National Policies

Because this was the first pandemic in modern time, the shutdowns were unprecedented. No one knew the potential financial, social, and medical impact. The financial costs of the shutdowns were obvious- closed restaurants, job furloughs, job loss, food pantry lines, etc. However, like most everything in public health, the health benefits were invisible. It’s hard to show the impact of infections and deaths that never occurred.

A VERY recent scientific study showed how the U.S. policies (and in other countries) impacted COVID19 spread. And, more interestingly, WHICH policies were most effective and which policies had NO impact on disease spread.

This figure comes directly from the preprint of the Nature article. See below for proper reference. In short, any horizontal line that does NOT cross the vertical line had an impact. So social distancing, quarantining, and working from home all WORKED. However, any horizontal line that DOES cross the vertical line had NO impact. So, for example, schools closing did not have an impact on COVID19 spread in the US.

Translation: Our sacrifices are/were worth it in regards to slowing down COVID19. Specifically, by social distancing, quarantining, and working from home, we were able to prevent 4.8 million diagnosed cases and 60 million actual cases. For the next wave or next pandemic, we can use this information to shut down more effectively.

Love, your local epidemiologist

Source: This was a peer reviewed article published in Nature, which can be found here: https://www.nature.com/arti…/s41586-020-2404-8_reference.pdf. The full reference is… Hsiang et al., (2020) The effect of large-scale anti-contagion policies on the COVID19-pandemic. Nature. https://doi.org/10.1038/s41586-020-2404-8