The burden of vector-borne diseases (Dengue, Zika virus, yellow fever, etc.) gradually increased in the past decade across the globe. Mathematical modelling on infectious diseases helps to study the transmission dynamics of the pathogens. Theoretically, the diseases can be controlled and eventually eradicated by maintaining the effective reproduction number, (R eff ), strictly less than 1. We established a vector-host compartmental model, and derived (R eff ) for vector-borne diseases. The analytic form of the (R eff ) was found to be the product of the basic reproduction number and the geometric average of the susceptibilities of the host and vector populations. The (R eff ) formula was demonstrated to be consistent with the estimates of the 2015-2016 yellow fever outbreak in Luanda, and distinguished the second minor epidemic wave. For those using the compartmental model to study the vector-borne infectious disease epidemics, we further remark that it is important to be aware of whether one or two generations is considered for the transition ''from host to vector to host'' in reproduction number calculation.
Background Associations between levels of various types of airborne particulate matter such as ambient PM2.5 and short-term mortality risk have been studied extensively. A metric called daily exceedance concentration hours (DECH) has been proved useful with respect to better modeling and understanding of acute mortality risk associated with pollution in southern Chinese cities. Notably however, it is unclear whether the strength of the association is timedependent. The current study investigated this using a comprehensive dataset acquired in Hong Kong spanning from 1999 to 2019. The methodology and modeling employed were similar to those used in prior studies. Methods Generalized additive models with quasi-Poisson distribution links were fitted to varying periods of an overall time series. These models were then examined to identify changes in implied effects on mortality risk over time. Results The replicated methodology of prior studies resulted in fairly consistent, but much reduced relative effects of DECH levels on mortality risk across the disease groups. The model remained significant with the inclusion of newer datasets. When applying the model to sliding time-windows of data, the effective risk of mortality remained relatively constant despite significantly changing levels of pollutants, especially with regard to mortality risk among cardiovascular diseases. Modelling other cause groups using DECH metrics yielded similar results to those acquired using other air pollution variables. Conclusion The results of the study support the use of DECH as a mortality risk factor, particularly with respect to cardiovascular diseases, and the size of the association is fairly consistent.
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