Susceptible supply limits the role of climate in the early SARS-CoV-2 pandemic

Abstract

Preliminary evidence suggests that climate may modulate the transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Yet it remains unclear whether seasonal and geographic variations in climate can substantially alter the pandemic trajectory, given that high susceptibility is a core driver. Here, we use a climate-dependent epidemic model to simulate the SARS-CoV-2 pandemic by probing different scenarios based on known coronavirus biology. We find that although variations in weather may be important for endemic infections, during the pandemic stage of an emerging pathogen, the climate drives only modest changes to pandemic size. A preliminary analysis of nonpharmaceutical control measures indicates that they may moderate the pandemic-climate interaction through susceptible depletion. Our findings suggest that without effective control measures, strong outbreaks are likely in more humid climates and summer weather will not substantially limit pandemic growth.

Fig. 1

Specific humidity and transmission. (A) Colored lines represent different hypotheses for the relationship between climate and transmission for SARS-CoV-2. Values of R₀ reflect SARS-CoV-2 estimates. The functional climate-dependence of influenza transmission, OC43 transmission, and HKU1 transmission is shown with solid, dashed, and dotted black lines, respectively. (B and C) A summary of seasonal model fits (blue line) for scaled average weekly cases (gray) of (B) OC43 and (C) HKU1. (Our model captures the biennial cycles of HKU1, shown in fig. S3, and detailed model fits for OC43, shown in fig. S2.) Coefficient of determination (R²) values are shown. I, number infected. (D and E) Fit results in terms of climate dependence and immunity length (weeks) for (D) OC43 and (E) HKU1, where mean fits are shown with dashed lines. MW, Midwest; W, West; N, North; S, South.

Fig. 2

Global model results and nine example trajectories. (A and B) The (A) maximum number of infections per capita (I/N) and (B) timing of peak I/N for global locations. Black circles show locations where trajectories are explicitly shown. The color scale shows maximum I/N in (A) and week of peak I/N in (B). (C to E) Simulated pandemics are shown for cities in (C) the northern hemisphere, (D) the southern hemisphere, and (E) tropical locations. The dotted line represents a pandemic with no climate dependence. NY, New York.

Fig. 3

Pandemic peak size depends on the proportion of the population that is susceptible. (A to C) For the three scenarios, (A) influenza, (B) OC43, and (C) HKU1, the surface plot shows the dependence of maximum pandemic incidence per capita on the seasonal range of humidity and the proportion of the population that is susceptible, assuming mean humidity of New York. params, parameters. (D) The time series from pandemic to endemic outbreaks for an example location (Wuhan with HKU1 params) (top) and the equivalent SI phase plane of pandemic and epidemic trajectories (bottom). The two nullclines are from the unforced SIRS using mean R₀. The green circle represents the equilibrium of the unforced model. S/N, proportion susceptible; $\dot{I}$, equilibrium infected; $\dot{S}$, equilibrium susceptible; N, total population. (E) The same trajectory but with a 6-month control period (reducing R₀ to 1.1). The yellow shading indicates the timing of the control period.

Fig. 4. Interaction between control measures and the climate.

(A and B) Four scenarios representing the interaction of different climate dependencies (OC43 and HKU1 params) with two potential control measures [R₀ =1.1 and 1.3 in the control period, occurring (A) 1 month and (B) 6 weeks after pandemic start]. The size and color of the circles represent the size of peak incidence (within 2 years of pandemic start) relative to the no-control scenario. White crosses show the month of maximum climate-driven transmission, i.e., lowest specific humidity.