How weather and demographics influence the COVID-19 spread

By Dr. Liji Thomas, MDApr 29 2020

What can stop the spread of the novel coronavirus? A new study published in the preprint journal medRxiv in April 2020 debunks the idea that warmer and more humid conditions can hinder the pandemic from spreading with ease.

Study: The dynamics of Covid-19: weather, demographics and infection timeline. Image Credit: DigitalMammoth

The COVID-19 pandemic has taken over public attention for months now, with its propensity to cause severe and often fatal pneumonic illness in the elderly, immunocompromised, and those with underlying medical conditions. The first cases occurred in China during December 2019, spreading rapidly after that to affect over 210 countries the world over.

While initially all cases in the southern hemisphere could be traced to people who had recently returned from China or other Asian countries, in every country thereafter, the picture has been of local community spread and rapid increase in the number of cases. In particular, once there are 100 cases, the graph becomes exponential over the next ten days. The only difference is in the rate of growth.

Brazil is one such country where the first case was identified on February 26, 2020, a traveler who had returned from northern Italy. By March 6, the spread was recognized to be through the community rather than from outside, and exponential increases were noted in the number of cases throughout March.

The fact noted by the investigators was that from the start of the outbreak until the end of March, Brazil experienced warm and humid weather. This suggests that the novel coronavirus SARS-CoV-2 will not be deterred by such conditions, unlike many other viruses.

How was the study done?

The current study is aimed at uncovering the effect of three different types of variables on the early growth of the outbreak, namely, the weather, including the temperature and absolute humidity, the population density, and the COVID-19 timeline. The researchers looked at cases in 50 states of the USA and another 110 countries with adequate records of these variables up to April 10.

They examined the case growth rate in terms of the exponential coefficient, beginning from the day of the 100^th case in each country, concerning these variables.

Heat and humidity

The results from the U.S. states show that the pandemic cannot be expected to slow down under conditions of heat and humidity within the ranges experienced in February and March 2020. The temperatures ranged from -2.4 to 24C and 2.3 to 15 g/m3 across the range of affected countries.

When only the weather variables are considered, it seems that warmer and more humid countries experience a lower rate of spread. This vanishes, however, with the addition of the timeline variable.

They comment: “In fact, the opposite is true: the higher the temperature and the absolute humidity, the faster the COVID-19 has expanded in the U.S. states, in the early stages of the outbreak.”

How does population density affect spread?

The second revelation is that the population density is the most significant predictor of the rapid early spread of the virus. A sparse population reduces the rates of contact, both from outside and within communities, slowing the rate of spread. Since such states would report their first case late in the outbreak, they would typically be already practicing social distancing at that point, further restricting viral transmission.

With every ten days of delay in the date of reporting the 100th case, the coefficient of expansion is reduced by 0.053 points and 0.045 points for the U.S. states and the countries, respectively. This shows the importance of social distancing, which creates an artificial sparseness of population, in reducing this coefficient. This, in turn, prevents an explosion of cases within a brief period, as happened in New York.

When the population density is doubled, the coefficient of expansion will go up by 0.011 points, and the 100th case will occur about two days earlier in this case.

What is the effect of the time to the first 100 cases?

And finally, the faster the country or state reaches the first 100 states, the higher is the speed of the outbreak’s spread. This variable is the only significant one for the countries in the study. It explains about a third of the variability of the rate of growth in the countries.

The reason to select this variable is that by this point, community spread is expected to be underway, accounting for the exponential rate of growth. The first 100 cases usually occur within one or a few communities, which points to the presence of local transmission.

Putting all the variables together, only the population density and timeline are significant statistically, accounting for over half of the differences between the growth rate in various U.S. states.

What are the implications?

The weather does not seem to play a role in determining the rate of the early spread of COVID-19. Instead, population density is key, both because of the characteristics of such communities that promote a more significant number of contacts, and the constant presence of travelers from outside.

For the U.S. states and countries, however, the delay to the 100^th case is the most crucial variable in determining the rate of growth of the outbreak. This may be because people and governments alike take the pandemic seriously at this stage and begin to take protective measures.

This is the most likely explanation because there is no sign that the virus is becoming less potent as of now. It is time to stop hoping that high temperature and humidity will help stop the virus because even hot, humid countries with low apparent rates of transmission are often those with low and unreliable reporting rates or those with the unavailability of testing.

The message the researchers want to pass on is that social distancing is the only strategy that seems to be working to reduce the scope of the outbreak in all countries and states where they are in place.

Important Notice

medRxiv publishes preliminary scientific reports that are not peer-reviewed and, therefore, not be regarded as conclusive, guide clinical practice/health-related behavior, or treated as established information.