Off-Topic Entirely – SARS-2/Covid19 Thoughts

I know what you are thinking. “I come to this blog to learn about making things blink, not get bummed out by health, politics, and economics.” And that’s fine. Writing this is maybe more an exercise for me than you. But my background and interests are pretty eclectic, and I’m using this as a chance to exercise parts of my brain that are directly relevant to the things that I am doing in class.

Introduction

There is a seemingly common misconception that an acute disease will affect all people and places equally, that it is, in essence, completely egalitarian in its destruction. The reality is, the impact of a disease is just as influenced by the populations it infects as its own virulence. A good example of this is HIV. In the US, HIV rates peaked at about 2% before declining. In certain African countries, HIV infection is as high as 25%. The difference? Despite knowing the same risks about infection, social pressures foster an environment conducive to mass infection. The virus is the same there as it is here. The outcomes are wildly different, due to a combination of demographics, cultural habits, and socioeconomic pressures. These items have a much higher influence on HIV outcomes than the virus itself!

Demographics, cultural habits drive epidemic outcomes

An interesting study is in pre-publication with regards to Covid-19 epidemiology in Italy. The authors note that the viral mechanics are similar to South Korea and China (eg, death rates, R0). But the thing that stands out is the effort the authors make to include demographics and cultural habits in the devastating effects the virus has had there.

Of particular note is the prevalence of inter-generational co-habitation. It is common that three generations of families live in a single household. In those cases where generations do not co-habitate, there is a strong preference for people to live in close proximity to extended family. Extended daily contact is common. By committing to living near family, commutes are correspondingly longer. All of this is relevant, because the longer one spends within 6ft of someone who is infected, the more likely they are to become infected themselves. That makes workplaces, public transportation, and domiciles the most likely places to pick up an infection. Close behind are bars and restaurants. 

Another factor working against Italy is the age structure of the population. Italy has one of the highest percentages of  people 65 and older.  As many as 30% of them are active cigarette smokers as well.

Contrast that with the demographics and social habits in America. Only 15% of US adults smoke, with age demographics skewed towards younger generations. The average American lives approximately 18 miles from a parent(s). Americans show preference for living closer to their workplaces and social interests. Family units and extended family do not have as important a priority for Americans as Italians, and family units are typically smaller. Americans in the 65 and older cohort tend to concentrate in communities tailored to their interests and needs. Americans also tend to primarily interact with members of their own age cohorts and peer groups. This is all reflected in data generated by the CDC. The majority of cases reported are in the youngest age cohort (20-44), the age cohort that has the highest level of peer cohabitation and social mobility. The second highest number of cases is reported from the 65-85 cohort, which, being composed primarily of retirees, is also characterized by close habitation and social mobility. Incidence in the highest risk group (85+) is by far the lowest. This is no surprise, seeing as how a high number of these individuals live in relative isolation in managed care facilities or specialty communities. Coupled to this, there are far fewer people in this cohort relative to Italy.

Some of this can be seen in the R0 estimates (this is the average number of people that an infected person will themselves infect). For China, a country where living quarters tend to be small, smoking incidence is high, and air pollution is some of the worst in the world, an R0 of approximately 2.5 and a doubling time of 6 days was initially reported. For the US, R0 is currently estimated at 2.0-2.2, with a doubling time of 7-10 days. Italy reported similar R0 to the US. India is currently reporting an R0 of about 1.7. 

Further complicating the picture is the state of testing in the US. Per capita testing in the US is a fraction of that in Italy. The total number of reported cases in the US is most likely skewed very low. Widespread testing is still not available, and testing capacity is reserved for the most severe symptoms. As of today, cumulative confirmed testing in the US has surpassed 14,000 cases. This surge can likely be attributed to an increase in testing availability as opposed to epidemiological factors. This puts the US at approximately the same number of cumulative infections as what Italy had on 12Mar. It would not be surprising to me if we exceeded Italy’s cumulative cases as more testing comes online, seeing as how there is significantly more travel between the US and China than China and Italy. To date, Italy has had 41,035 cumulative cases and over 3,400 deaths, with 2,498 cases severe enough to warrant ICU admittance. On 12Mar, Italy had 1,016 confirmed deaths, 15,113 cumulative cases, and 12,839 active cases. Yet today, the US has 14,299 confirmed cumulative cases, 13,960 active cases, and only 150 cumulative deaths, with only 64 active cases severe enough to warrant admittance to an ICU. Clearly, demographics and cultural play a major role in epidemic outcomes.

How this should affect policy?

Policy is typically set by minimizing negative outcomes first, and societal impact second. Minimizing total number of cases a primary concern except in the most virulent of epidemics, but is rather the purview of future prophylactic actions such as vaccines and social engineering. While it is understandable that in the absence of data, a worst-case approach is the only prudent decision, with recent improvements in demographic analysis and data collection, policy should be shifted. Once testing capacity can meet demand, a regime of quarantining of retirement community and nursing homes, paid sick leave, testing and surveillance, and self-isolation will likely be as or more effective in preventing negative outcomes than the current regime of shelter-in-place and economic shutdown.

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