Impact on Vector-Borne Diseases in COVID-19 Pandemic: Evidence from a District in Jharkhand, India

The study is aimed to assess the effect of COVID-19 pandemic and non-pharmaceutical interventions (NPI) like banning mass gatherings (Lockdown) etc on vector borne diseases. This study can throw some light on the epidemiology of all vector-borne diseases under surveillance during this COVID-19 pandemic. This study is done in Dumka district, Jharkhand. Method- A cross-sectional study was conducted in 10 Blocks of Dumka District. Sampling technique used in this study was convenience sampling. Study of six month was conducted for vector-borne diseases from January 2021– June 2021.To know the epidemiology of vector-borne disease before and after COVID-19 pandemic, data from the year 2019 and 2020 was used for data analysis. Result – During the six months study period between January 2021-June 2021, maximum number of cases found in Dumka District was Kala-azar followed by Lymphatic Filariasis and then Malaria. There was sharp decrease in number of vector-borne disease cases. After data analysis between the year 2019 and 2020 reduction of cases was seen in Kala-azar (15.3%), Lymphatic Filariasis (8.9%) and maximum reduction of cases was seen Malaria (98.1%). Conclusion – A drastic reduction in reported cases of vector-borne diseases was noticed. To better understand the reason behind the changes in vector-borne disease prevalence a consistent and vigilant surveillance is required. Keywords: COVID-19, Vector-borne diseases, non-pharmaceutical interventions.

Immunomodulation by Mosquito Salivary Protein AgSAP Contributes to Early Host Infection by Plasmodium

Malaria is a vector-borne disease caused by Plasmodium sporozoites. When an anopheline mosquito bites its host, it releases Plasmodium sporozoites as well as saliva components.

Interfacing vector-borne disease dynamics with climate change: Implications for the attainment of SDGs in Masvingo city, Zimbabwe

This study used a mixed-methods research design to examine the sensitivity of vector-borne disease (VBD) patterns to the changes in rainfall and temperature trends. The research focused on malaria in Masvingo Province, Zimbabwe. The study interfaced the climate action, health and sustainable cities and communities with sustainable development goals (SDGs). Historical climate and epidemiological data were used to compute the correlations and determine the possible modifications of disease patterns. Clustered random and chain-referral sampling approaches were used to select study sites and respondents. Primary data were gathered through a questionnaire survey (n = 191), interviews and focus group discussions, with Mann–Kendal trend tests performed using XLSTAT 2020. The results show a positive correlation between malaria prevalence rates and temperature-related variables. A decline in precipitation-related variables, specifically mean monthly precipitation (MMP), was associated with an increase in malaria prevalence. These observations were confirmed by the views of the respondents, which show that climate change has a bearing on malaria spatial and temporal dynamics in Masvingo Province. The study concludes that climate change plays a contributory role in VBD dynamics, thereby impeding the attainment of the 2030 Agenda for Sustainable Development, especially SDG 3, which deals with health. The study recommends further research into appropriate adaptation mechanisms to increase the resilience of rural and urban communities against the negative transmutations associated with weather and climatic pressures.

Using Multiple Scale Space-Time Patterns to Determine the Number of Replicates and Burn-In Periods in Spatially Explicit Agent-Based Modeling of Vector-Borne Disease Transmission

(1) Background: The stochastic nature of agent-based models (ABMs) may be responsible for the variability of simulated outputs. Multiple simulation runs (i.e., replicates) need to be performed to have enough sample size for hypothesis testing and validating simulations. The simulation outputs in the early-stage of simulations from non-terminating ABMs may be underestimated (or overestimated). To avoid this initialization bias, the simulations need to be run for a burn-in period. This study proposes to use multiple scale space-time patterns to determine the number of required replicates and burn-in periods in spatially explicit ABMs, and develop an indicator for these purposes. (2) Methods: ABMs of vector-borne disease transmission were used as the case study. Particularly, we developed an index, D, which enables to take into consideration a successive coefficient of variance (CV) over replicates and simulation years. The comparison between the number of replicates and the burn-in periods determined by D and those chosen by CV was performed. (3) Results: When only a single pattern was used to determine the number of replicates and the burn-in periods, the results varied depending on the pattern. (4) Conclusions: As multiple scale space-time patterns were used for the purposes, the simulated outputs after the burn-in periods with a proper number of replicates would well reproduce multiple patterns of phenomena. The outputs may also be more useful for hypothesis testing and validation.