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ZHAO, S. F.

Publications and source records attributed to ZHAO, S. F..

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New estimates of the Zika virus epidemic attack rate in Northeastern Brazil from 2015 to 2016: A modelling analysisbased on Guillain-Barre Syndrome (GBS) surveillance data

BackgroundBetween January 2015 and August 2016, two epidemic waves of Zika virus (ZIKV) disease swept the Northeastern region of Brazil. As a result, two waves of Guillain-Barre Syndrome (GBS), were observed concurrently. The mandatory reporting of ZIKV disease began region-wide in February 2016, and it is believed that ZIKV cases were significantly under-reported before that. The changing reporting rate has made it difficult to estimate the ZIKV infection attack rate, and studies in the literature vary widely from 17% to > 50%. The same applies for other key epidemiological parameters. In contrast, the diagnosis and reporting of GBS cases were reasonably reliable given the severity and easy recognition of the diseases symptoms. In this paper, we aim to estimate the real number of ZIKV cases (i.e., the infection attack rate), and their dynamics in time, by scaling up from GBS surveillance data in NE Brazil.\n\nMethodologyA mathematical compartmental model is constructed that makes it possible to infer the true epidemic dynamics of ZIKV cases based on surveillance data of excess GBS cases. The model includes the possibility that asymptomatic ZIKV cases are infectious. The model is fitted to the GBS surveillance data and the key epidemiological parameters are inferred by using the plug-and-play likelihood-based estimation. We make use of regional weather data to determine possible climate-driven impacts on the reproductive number [R]0, and to infer the true ZIKV epidemic dynamics.\n\nFindings and ConclusionsThe GBS surveillance data can be used to study ZIKV epidemics and may be appropriate when ZIKV reporting rates are not well understood. The overall infection attack rate (IAR) of ZIKV is estimated to be 24.1% (95% CI: 17.1% - 29.3%) of the population. By examining various asymptomatic scenarios, the IAR is likely to be lower than 33% over the two ZIKV waves. The risk rate from symptomatic ZIKV infection to develop GBS was estimated as{rho} = 0.0061% (95% CI: 0.0050% - 0.0086%) which is significantly less than current estimates. We found a positive association between local temperature and the basic reproduction number, [R]0. Our analysis revealed that asymptomatic infections affect the estimation of ZIKV epidemics and need to also be carefully considered in related modelling studies. According to the estimated effective reproduction number and population wide susceptibility, we comment that a ZIKV outbreak would be unlikely in NE Brazil in the near future.\n\nAuthor SummaryThe mandatory reporting of Zika virus (ZIKV) disease began region-wide in February 2016, and it is believed that ZIKV cases could have been highly under-reported before that. Given the Guillain-Barre syndrome (GBS) is relatively well reported, the GBS surveillance data has the potential to act as a reasonably reliable proxy for inferring the true ZIKV epidemics. We developed a mathematical model incorporating the weather effects to study the ZIKV-GBS epidemics and estimated the key epidemiological parameters. We found the attack rate of ZIKV is likely lower than 33% over the two epidemic waves. The risk rate from symptomatic ZIKV case to develop GBS is likely 0.0061%. According to the analysis, we comment that there would be difficult for a ZIKV outbreak to appear in NE Brazil in the near future.

epidemiology

Large-scale Lassa fever outbreaks in Nigeria: quantifying the association between disease reproduction number and local rainfall

BackgroundLassa fever (LF) is increasingly recognized as an important rodent-borne viral hemorrhagic fever presenting a severe public health threat to sub-Saharan West Africa. In 2018, LF caused an unprecedented outbreak in Nigeria, and the situation was worse in 2019. This work aims to study the epidemiological features of outbreaks in different Nigerian regions and quantify the association between reproduction number (R) and local rainfall by using modeling analysis.\n\nMethodsWe quantify the infectivity of LF by the reproduction numbers estimated from four different growth models: the Richards, three-parameter logistic, Gompertz, and Weibull growth models. LF surveillance data are used to fit the growth models and estimate the Rs and epidemic turning points ({tau}) in different regions at different time periods. Cochrans Q test is further applied to test the spatial heterogeneity of the LF epidemics. A linear random-effect regression model is adopted to quantify the association between R and local rainfall with various lag terms.\n\nFindingsOur estimated Rs for 2017-18 (1.33 with 95% CI: [1.29, 1.37]) and 2018-19 (1.29 with 95% CI: [1.27, 1.32]) are significantly higher than those for 2016-17 (1.23 with 95% CI: [1.22, 1.24]). We report spatial heterogeneity in the Rs for outbreaks in different Nigerian regions. For the association between rainfall and R, we find that a one unit (mm) increase in average rainfall over the past 7 months could cause a 0.62% (95% CI: [0.20%, 1.05%]) rise in R.\n\nConclusionThere is significant spatial heterogeneity in the LF epidemics in different Nigerian regions. We report clear evidence of rainfall impacts on LF outbreaks in Nigeria and quantify the impact.

epidemiology