Fig 1.
Compartmental model of transmission.
SH, EH, IH, and RH represent the susceptible, exposed (or latent), infectious, and recovered segments of the human population, respectively. Likewise, SV, EV, and IV represent the susceptible, exposed (or latent), and infectious segments of the mosquito population. Solid arrows signify the directionality of transition from one compartment to the next, and dashed arrows indicate the directionality of transmission.
Table 1.
Fitted thermal responses for Aedes aegypti life history traits.
Traits were fit to a Brière [] or a quadratic [c(T − Tm)(T − T0)] function where T represents temperature. T0 and Tm are the critical thermal minimum and maximum, respectively, and c is the rate constant. Thermal responses were fit by [24].
Table 2.
Values of temperature-independent parameters used in the model, and their sources.
Table 3.
Temperature regimes for major cities during the 2016 calendar year.
Monthly mean temperatures during 2016 were extracted from Weather Underground.
Fig 2.
Variation in epidemic dynamics by temperature.
The model was simulated under default parameters at four constant temperatures: 20°C, 25°C, 30°C, and 35°C.
Fig 3.
Epidemiological indices as a function of starting temperature, within a given seasonal temperature regime.
The red curve represents the maximum number of humans in the infected class (IH) at any given point during the simulation. The blue curve represents the final (or cumulative) epidemic size (RH at the final time step). The green curve represents the length of the epidemic (i.e., the point at which the number of infected individuals was below one). Here, simulations were run with the temperature conditions: Tmin = 10°C, Tmean = 25°C, and Tmax = 40°C (A) and Tmin = 20°C, Tmean = 25°C, and Tmax = 30°C (B).
Table 4.
Estimates of epidemic suitability for major cities.
Epidemic suitability was calculated as the proportion of the population that became infected in simulations run with 0, 20, 40, 60, or 80% initial population immunity. Temperature at simulation onset was set to the mean of the temperature regime. Each city was simulated with its respective temperature regime from the 2016 calendar year.
Fig 4.
Variation in epidemic suitability across different seasonal temperature regimes.
The heat map shows the epidemic suitability (represented as the proportion of the total human population infected during an epidemic) as a function of mean annual temperature and temperature range. Here, temperature range is defined as the seasonal variation about the annual mean temperature. Twenty large, globally important cities are plotted to illustrate their epidemic suitability.
Fig 5.
Variation in epidemic suitability across different seasonal temperature regimes averaged across starting temperatures.
The heat map shows the epidemic suitability (represented as the proportion of the total human population infected during an epidemic) as a function of mean annual temperature and temperature range averaged across simulations where the initial temperature was set to the seasonal temperature regime’s minimum, mean, or maximum temperature. Here, temperature range is defined as the seasonal variation about the annual mean temperature. Twenty large, globally important cities are plotted to illustrate their epidemic suitability.
Table 5.
Estimates of epidemic suitability for major cities under different starting temperatures.
Epidemic suitability was calculated as the proportion of the population that became infected in simulations that began at the minimum, mean, or maximum temperature of the seasonal temperature regime. Each city was simulated with its respective temperature regime from the 2016 calendar year with 0% population immunity.