Ecological and epidemiological models are both useful for SARS-CoV-2

A critical issue for top-down pattern analysis is whether variables of interest, like climate⁵ or concentration of pollutants⁶, have a mechanistic link to spread of the disease and how much that link affects transmission patterns. Using the DOTS equation and focusing on climate, we can speculate about the mechanistic links of SARS-CoV-2 and climate.

Firstly, it is unclear what the effects of climate might be on the duration, D, of the infectious period. However, it is not unusual that, under optimal climate conditions, immunological responses to respiratory diseases are stronger, thus reducing the period of infectiousness⁷.

Secondly, opportunities for infection, O, are related to human behaviours modulating the magnitude and direction of contacts. For example, during winter, people cluster indoors, thus increasing the chance of contacts⁸. But indoor clustering might also occur under warm and wet conditions. Although climate affects human behaviour, including mobility, it is unlikely that a unique relationship would emerge worldwide.

Thirdly, transmission probability, T, is strongly affected by protective measures (for example, wearing masks, gloves and cleaning hands). However, if climate affects the viability of the virus outside the human body, it could affect transmission by contact with surfaces and aerosols⁹. In such cases, T could vary across climatic gradients.

Finally, susceptibility to the virus, S, is directly linked to the immune response of the host. While it is unclear whether geographical clustering of immune responses to SARS-CoV-2 exists, it is foreseeable that they could be affected by climate¹⁰. Such a response would affect D as well as S.

The above discussion illustrates possible connections between SARS-CoV-2 and climate. Statistical relationships between SARS-CoV-2 and predictor variables of interest will stabilize as more data become available. So far, analyses cover the initial phases of the pandemic and there is substantial noise in key parameters. For example, during April–May 2020 the United States had the highest world COVID-19 mortality and Brazil the highest mortality in South America. This was likely related to the type of non-pharmaceutical responses to the disease (terms O and T in the DOTS equation). Nevertheless, signals emerge regionally and globally such that environmental factors could be linked to spread of SARS-CoV-2 and the severity of its effects. Addressing these questions will require continuous research.