The Role Of Temperature In Covid-19 Transmission Rates

Asist.Prof.Dr.Ahmet ÖZYİĞİT

Since the first identified case in December 2019 in China, Covid-19 has spread globally, earning itself a pandemic classification. As of mid-March, the total number of cases in China has been surpassed by those in the rest of the world. Currently, there are over 800,000 cases worldwide with an overall mortality rate of five percent1.

In-vitro studies suggest a role for meteorological conditions on coronavirus survival2 which makes temperature an ideal candidate for investigation for a better understanding of viral transmission rates. A recent study covering 26 countries with reported SARS-CoV-2 positive cases has found no significant relationship between the spread of the virus and air temperature3. However, the study covers a brief timeline with limited temperature variability as well as viral outreach. Focusing only on China, a newer study finds evidence of a significant impact of temperature on case doubling times where a 20-degree Celsius increase in temperature is estimated to increase doubling times by 1·8 days4. Cold exposure is also implicated to play a role in innate human immunity besides virus survival rates. Cold temperatures tend to reduce the blood supply and provision of immune cells to the nasal mucosa, making people more susceptible to microbial infections in winter time5.

In an attempt to explain the role of temperature in disease transmission, this brief study uses a panel data approach to study the effect of daily temperatures on the rate of spread of Covid-19 across the ten countries with a 30-day disease prevalence history. Each country has its own containment measure and some use draconian measures while others rely on lighter precautions. Moreover, the number of tests performed per capita highly differs across countries. Therefore, relying on absolute case numbers would highly undermine the reliability of any empirical work. Instead, the case numbers in each country has been first differenced in order to capture the rate of change as opposed to absolute number of cases. A panel data regression is estimated to explain rate of Covid-19 transmission as a function of temperature and the length of containment measures used during the epidemic. The results of the panel data estimation are provided in table 1:
Table 1: Panel Data Estimates

  Pooled Panel Pooled Panel Random Effects Random Effects Fixed Effects
Prevalence = f(Temp) Prevalence = f(Temp, Containment) Prevalence = f(Temp) Prevalence = f(Temp, Containment) Prevalence = f(Temp)
Constant  0·270 ***  0·304 ***  0·309 ***  0·315 ***  0·364 ***
Temperature -0·004 *** -0·003 ** -0·007 ** -0·005 * -0·010 ***
Containment Measures --------------- -0·114 *** ---------------- -0·082 *** (Already embedded in model)
R2  0·025  0·087  0·018  0·039  0·193
Note: *, ** and *** denote significance at 10%, 5% and 1%, respectively.
Random and fixed effects models both acknowledge country heterogeneities while the pooled panel estimates assume homogeneous cross-sections. The fixed effects model also introduces time-based heterogeneities as well as cross-sectional ones, which is more useful in settings where dynamic changes are occurring. Nevertheless, in all the models estimated, we see a small but statistically significant negative relationship between temperature and the rate of disease transmission. In the fixed effects model, controlling for cross-sectional and time-based heterogeneities, it appears that 19 percent of the rate of transmission can be explained by temperature differentials alone. In the rest of the estimates, we see that containment measures are successful in reducing the rate of disease transmission. As more data becomes available in the upcoming days, more can be obtained from follow-up studies.

1. WHO. Coronavirus disease 2019 (COVID-19) Situation Report – 59. 2020; published online March 19. (accessed March 19, 2020).
2. Casanova LM, Jeon S, Rutala WA, Weber DJ, Sobsey MD. Effects of Air Temperature and Relative Humidity on Coronavirus Survival on Surfaces. Applied and Environmental Microbiology 2010; 76: 2712–7.
3. Wang M, Jiang A, Gong L, et al. Temperature significant change COVID-19 Transmission in 429 cities. DOI:10.1101/2020.02.22.20025791.
4. Oliveiros B, Caramelo L, Ferreira NC, Caramelo F. Role of temperature and humidity in the modulation of the doubling time of COVID-19 cases. DOI:10.1101/2020.03.05.20031872.
5. Sun Z, Thilakavathy K, Kumar SS, He G, Liu SV. Potential Factors Influencing Repeated SARS Outbreaks in China. IJERPH17: 1633.