Test for Covid-19 seasonality and the risk of second waves

Abstract

Ten months into the Covid-19 pandemic it remains unclear whether transmission of SARS-CoV-2 is affected by climate factors. Using a dynamic epidemiological model with Covid-19 climate sensitivity in the likely range, we demonstrate why attempts to detect a climate signal in Covid-19 have thus far been inconclusive. Then we formulate a novel methodology based on susceptible-infected time trajectories that can be used to test for seasonal climate sensitivity in observed Covid-19 infection data. We show that if the disease does have a substantial seasonal dependence, and herd immunity is not established during the first peak season of the outbreak (or a vaccine does not become available), there is likely to be a seasonality-sensitive second wave of infections about one year after the initial outbreak. In regions where non-pharmaceutical control has contained the disease in the first year of outbreak and thus kept a large portion of the population susceptible, the second wave may be substantially larger in amplitude than the first if control measures are relaxed. This is simply because it develops under the favorable conditions of a full autumn to winter period and from a larger pool of infected individuals.

Fig. 1

Susceptible-Infected (SI) orbits for various scenarios of non-pharmaceutical control measures and seasonality of ε = 0.2, for the case of mid-winter onset of an infectious disease. The orbits in black represent the scenario of no control with range and R₀ interval [1.4, 3; 0.1] in a) and [1.4, 2; 0.1] in b) and c). Note the different range of I/N in a) compared to b) and c). The colored lines orbits represent a) effects of seasonality for the scenario of no control; b) effects of a lockdown followed by social distancing and c) effects of lockdown followed by social distancing in combination with seasonality. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 2

Evolution of I/N for an infectious disease for different values of R₀ and where onset occurs in mid-winter. The black lines represents the scenario of no control measures and no seasonality, the green lines represent no control measures but with seasonality effects included, the yellow lines represent non-pharmaceutical control measures (see the text for details) and the red lines indicate the effects of seasonality in combination with non-pharmaceutical control. Seasonality is for ε = 0.2. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 3

Susceptible-Infected (SI) orbits for various scenarios of non-pharmaceutical control measures and seasonality of ε = 0.2, for the case of mid-summer onset of an infectious disease. The orbits in black represent the scenario of no control with range and R₀ interval [1.4, 3; 0.1] in a) and [1.4, 2; 0.1] in b) and c). Note the different range of I/N in a) compared to b) and c). The colored lines orbits represent a) effects of seasonality for the scenario of no control; b) effects of a lockdown followed by social distancing and c) effects of lockdown followed by social distancing in combination with seasonality. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 4

Evolution of I/N for an infectious disease for different values of R₀ and where onset occurs in mid-summer. The black lines represents the scenario of no control measures and no seasonality, the green lines represent no control measures but with seasonality effects included, the yellow lines represent non-pharmaceutical control measures (see the text for details) and the red lines indicate the effects of seasonality in combination with non-pharmaceutical control. Seasonality is for ε = 0.2. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)