Temperature modulates dengue virus epidemic growth rates through its effects on reproduction numbers and generation intervals

Abstract

Epidemic growth rate, r, provides a more complete description of the potential for epidemics than the more commonly studied basic reproduction number, R0, yet the former has never been described as a function of temperature for dengue virus or other pathogens with temperature-sensitive transmission. The need to understand the drivers of epidemics of these pathogens is acute, with arthropod-borne virus epidemics becoming increasingly problematic. We addressed this need by developing temperature-dependent descriptions of the two components of r—R0 and the generation interval—to obtain a temperature-dependent description of r. Our results show that the generation interval is highly sensitive to temperature, decreasing twofold between 25 and 35°C and suggesting that dengue virus epidemics may accelerate as temperatures increase, not only because of more infections per generation but also because of faster generations. Under the empirical temperature relationships that we considered, we found that r peaked at a temperature threshold that was robust to uncertainty in model parameters that do not depend on temperature. Although the precise value of this temperature threshold could be refined following future studies of empirical temperature relationships, the framework we present for identifying such temperature thresholds offers a new way to classify regions in which dengue virus epidemic intensity could either increase or decrease under future climate change.

Assessing dengue control in Tokyo, 2014

“…Considering the published length of the EIP [38,39,47], with summer temperatures in metropolitan Tokyo above 30˚C in August, a mean generation time of about 2 weeks is regarded as a reasonable length or slightly shorter than the temperature-dependent estimate. Our estimate was consistent with an empirical estimate by Siraj et al [48], indicating that a mean generation interval of 17 days occurs with the highest probability at 30˚C, although the estimate was obtained for Aedes aegypti and not for Aedes albopictus, the latter of which is abundant in Japan. The mean incubation period of 5.8 days is also consistent with the literature [37], and we attained a finer estimation than that of Ishikawa et al [30]; those authors used only known 67 intrinsic incubation periods for the estimation (with an estimated mean of 6.3 days), potentially resulting in a biased estimate of the variance owing to small sample size.…”

Thermal biology of mosquito‐borne disease

“…Similarly, for dengue, chikungunya, and Zika viruses in Ae. aegypti , previous models with more limited thermal biology assumptions predicted thermal optima for transmission up to 6ºC higher than our published R 0 model, which peaked at 29ºC (Table ) (Johansson et al ; Liu‐Helmersson et al ; Morin et al ; Wesolowski et al ; Caminade et al ; Siraj et al ). We found a strong positive relationship between predicted temperature‐dependent R 0 and human incidence of dengue (> 85% accuracy) and chikungunya and Zika (> 66% accuracy) across the Americas in 2014–2016 (Fig. )…”

Inter-annual variation in seasonal dengue epidemics driven by multiple interacting factors in Guangzhou, China

“…where Eis the probability that the serial interval is / days 47 . We informed the values of Ethat describe the length of the serial interval using a probability density function derived by Siraj et al 48 based on first-principles assumptions about DENV transmission. This formulation takes into account lags associated with DENV incubation in humans (intrinsic incubation period), DENV incubation in mosquitoes (extrinsic incubation period), and mosquito longevity, resulting in a probabilistic summary of the time that elapses between one human case and another.…”

“…Although the role of these factors in driving transmission is commonly assumed by models 51 and consistent with the highly seasonal nature of DENV transmission in Guangzhou 17 , it is also clear that these factors may influence transmission considerably in advance of a case occurring. For example, high mosquito densities would be expected to affect transmission 2-3 weeks in advance, rather than instantaneously, to allow mosquitoes sufficient time to become infected, incubate the virus, and transmit it 48 .…”