This paper examines the impact of non-pharmaceutical intervention by government on stock market return as well as volatility. Using daily Malaysian equity data from January 28, 2020 to May 31, 2022, the regression analysis with bootstrapping technique reveals that the government's response in combating the deadly virus through Stringency index has shown a positive direct effect on both stock market returns and volatility, and indirect negative effect on stock market returns. The study revealed that international travel restriction and cancelling public events are the major contributors to the growth of volatility when estimated for Malaysia stock market index. On the one hand, heterogenous impact is expected from the perspective of different sectors when the individual social distancing measures were taken into account in determining stock return and volatility. Apart from that, the robustness check for the main findings remains intact in majority of the regression models after incorporating daily COVID-19 death rate, log (daily vaccination) and day-of-the-week effect as additional control variable in alternative.
The complex transmission ecologies of vector-borne and zoonotic diseases pose challenges to their control, especially in changing landscapes. Human incidence of zoonotic malaria ( Plasmodium knowlesi) is associated with deforestation although mechanisms are unknown. Here, a novel application of a method for predicting disease occurrence that combines machine learning and statistics is used to identify the key spatial scales that define the relationship between zoonotic malaria cases and environmental change. Using data from satellite imagery, a case-control study, and a cross-sectional survey, predictive models of household-level occurrence of P. knowlesi were fitted with 16 variables summarized at 11 spatial scales simultaneously. The method identified a strong and well-defined peak of predictive influence of the proportion of cleared land within 1 km of households on P. knowlesi occurrence. Aspect (1 and 2 km), slope (0.5 km) and canopy regrowth (0.5 km) were important at small scales. By contrast, fragmentation of deforested areas influenced P. knowlesi occurrence probability most strongly at large scales (4 and 5 km). The identification of these spatial scales narrows the field of plausible mechanisms that connect land use change and P. knowlesi, allowing for the refinement of disease occurrence predictions and the design of spatially-targeted interventions.