The impact of climate variability on infectious disease transmission in China: Current knowledge and further directions

Yi, Liping; Xu, Xin; Ge, Wenxin; Xue, Hong; Jin, Li; Li, Daoyuan; Wang, Chunping; Wu, Haixia; Liu, Xiaobo; Zheng, Dashan; Chen, Zhe; Liu, Qiyong; Bi, Peng; Li, Jing

doi:10.1016/j.envres.2019.03.043

Cited by 52 publications

(33 citation statements)

References 63 publications

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“…The number of dengue cases generally started dropping when mean temperature reached a high level in prior studies but we did not observe this pattern in our study. We also found that the relationship between rainfall and the number of dengue cases was bell-shaped, which is in line with the findings in China (Yi et al 2019) and the Philippines (Iguchi et al 2018). Previous studies examining the associations of climatic factors with dengue in Indonesia found contrasting results in different regions.…”

Section: Discussionsupporting

confidence: 90%

Using dengue epidemics and local weather in Bali, Indonesia to predict imported dengue in Australia

Bambrick

Yakob

et al. 2019

Environmental Research

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Background: Although the association between dengue in Bali, Indonesia, and imported dengue in Australia has been widely asserted, no study has quantified this association so far. Methods: Monthly data on dengue and climatic factors over the past decade for Bali and Jakarta as well as monthly data on imported dengue in Australia underwent a three-stage analysis. Stage I: a quasi-Poisson regression with distributed lag non-linear model was used to assess the associations of climatic factors with dengue in Bali. Stage II: a generalized additive model was used to quantify the association of dengue in Bali with imported dengue in Australia with and without including the number of travelers in log scale as an offset. Stage III: the associations of mean temperature and rainfall (two climatic factors identified in stage I) in Bali with imported dengue in Australia were examined using stage I approach. Results: The number of dengue cases in Bali increased with increasing mean temperature, and, up to a certain level, it also increased with increasing rainfall but dropped off for high levels of rainfall. Above a monthly incidence of 1.05 cases per 100,000, dengue in Bali was almost linearly associated with imported dengue in Australia at a lag of one month. Mean temperature (relative risk (RR) per 0.5 °C increase: 2.95, 95% confidence interval (CI): 1.87, 4.66) and rainfall (RR per 7.5 mm increase: 3.42, 95% CI: 1.07, 10.92) in Bali were significantly associated with imported dengue in Australia at a lag of four months. Conclusions: This study suggests that climatic factors (i.e., mean temperature and rainfall) known to be conducive of dengue transmission in Bali can provide an early warning with 4month lead time for Australia in order to mitigate future outbreaks of local dengue in Australia. 4 This study also provides a template and framework for future surveillance of travel-related infectious diseases globally.

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Section: Discussionsupporting

confidence: 90%

Using dengue epidemics and local weather in Bali, Indonesia to predict imported dengue in Australia

Bambrick

Yakob

et al. 2019

Environmental Research

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“…West Nile virus is by far the most studied enzootic arbovirus (Table 3). Models have been reviewed for WNV [15,149,163,168], Japanese encephalitis virus (JEV) [15,16,20], RVFV [14,15], RRV [7], and Barmah forest virus (BFV) [7,169,170]. Modeling studies on temperature for remaining viruses include: EEEV [3,171], LACV [13], MVEV [3], Sindbis virus (SINV) [3,172,173], St. Louis encephalitis virus (SLEV) [3,174], and Western equine encephalitis virus (WEEV) ([174,175,176] and citations therein, [3]).…”

Section: Viral Distributionmentioning

confidence: 99%

The Role of Temperature in Transmission of Zoonotic Arboviruses

Ciota

Keyel

2019

Viruses

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We reviewed the literature on the role of temperature in transmission of zoonotic arboviruses. Vector competence is affected by both direct and indirect effects of temperature, and generally increases with increasing temperature, but results may vary by vector species, population, and viral strain. Temperature additionally has a significant influence on life history traits of vectors at both immature and adult life stages, and for important behaviors such as blood-feeding and mating. Similar to vector competence, temperature effects on life history traits can vary by species and population. Vector, host, and viral distributions are all affected by temperature, and are generally expected to change with increased temperatures predicted under climate change. Arboviruses are generally expected to shift poleward and to higher elevations under climate change, yet significant variability on fine geographic scales is likely. Temperature effects are generally unimodal, with increases in abundance up to an optimum, and then decreases at high temperatures. Improved vector distribution information could facilitate future distribution modeling. A wide variety of approaches have been used to model viral distributions, although most research has focused on the West Nile virus. Direct temperature effects are frequently observed, as are indirect effects, such as through droughts, where temperature interacts with rainfall. Thermal biology approaches hold much promise for syntheses across viruses, vectors, and hosts, yet future studies must consider the specificity of interactions and the dynamic nature of evolving biological systems.

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“…In recent years, the relationship between weather variability and contagious diseases has received increasingly wide attention, because climatic drivers may play a pivotal role in the dynamics, distribution, and transmission of contagious diseases via impacting the growth and development of pathogenic agents, population dynamics of hosts and human behaviors, and thus can be deemed as early warning signals for the dynamic epidemics of contagious diseases 15,16 . Presently, many publications have found that meteorological parameters are closely related to the incidences of scarlet fever 17 , tuberculosis 18 , dengue fever 19 , hemorrhagic fever with renal syndrome (HFRS) 20 , bacillary dysentery 21 , human brucellosis 22 , hand, foot and mouth disease (HFMD) 23 , etc.…”

mentioning

confidence: 99%

The long-term effects of meteorological parameters on pertussis infections in Chongqing, China, 2004–2018

Wang

Ren

et al. 2020

Sci Rep

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Evidence on the long-term influence of climatic variables on pertussis is limited. This study aims to explore the long-term quantitative relationship between weather variability and pertussis. Data on the monthly number of pertussis cases and weather parameters in Chongqing in the period of 2004–2018 were collected. Then, we used a negative binomial multivariable regression model and cointegration testing to examine the association of variations in monthly meteorological parameters and pertussis. Descriptive statistics exhibited that the pertussis incidence rose from 0.251 per 100,000 people in 2004 to 3.661 per 100,000 persons in 2018, and pertussis was a seasonal illness, peaked in spring and summer. The results from the regression model that allowed for the long-term trends, seasonality, autoregression, and delayed effects after correcting for overdispersion showed that a 1 hPa increment in the delayed one-month air pressure contributed to a 3.559% (95% CI 0.746–6.293%) reduction in the monthly number of pertussis cases; a 10 mm increment in the monthly aggregate precipitation, a 1 °C increment in the monthly average temperature, and a 1 m/s increment in the monthly average wind velocity resulted in 3.641% (95% CI 0.960–6.330%), 19.496% (95% CI 2.368–39.490%), and 3.812 (95% CI 1.243–11.690)-fold increases in the monthly number of pertussis cases, respectively. The roles of the mentioned weather parameters in the transmission of pertussis were also evidenced by a sensitivity analysis. The cointegration testing suggested a significant value among variables. Climatic factors, particularly monthly temperature, precipitation, air pressure, and wind velocity, play a role in the transmission of pertussis. This finding will be of great help in understanding the epidemic trends of pertussis in the future, and weather variability should be taken into account in the prevention and control of pertussis.

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The impact of climate variability on infectious disease transmission in China: Current knowledge and further directions

Cited by 52 publications

References 63 publications

Using dengue epidemics and local weather in Bali, Indonesia to predict imported dengue in Australia

Using dengue epidemics and local weather in Bali, Indonesia to predict imported dengue in Australia

The Role of Temperature in Transmission of Zoonotic Arboviruses

The long-term effects of meteorological parameters on pertussis infections in Chongqing, China, 2004–2018

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