Malaria risk is highly heterogeneous. Understanding village and household-level spatial heterogeneity of malaria risk can support a transition to spatially targeted interventions for malaria elimination. This analysis uses data from cross-sectional prevalence surveys conducted in 2014 and 2016 in two villages (Megiar and Mirap) in Papua New Guinea. Generalised additive modelling was used to characterise spatial heterogeneity of malaria risk and investigate the contribution of individual, household and environmental-level risk factors. Following a period of declining malaria prevalence, the prevalence of P. falciparum increased from 11.4 to 19.1% in Megiar and 12.3 to 28.3% in Mirap between 2014 and 2016, with focal hotspots observed in these villages in 2014 and expanding in 2016. Prevalence of P. vivax was similar in both years (20.6% and 18.3% in Megiar, 22.1% and 23.4% in Mirap) and spatial risk heterogeneity was less apparent compared to P. falciparum. Within-village hotspots varied by Plasmodium species across time and between villages. In Megiar, the adjusted odds ratio (AOR) of infection could be partially explained by household factors that increase risk of vector exposure, such as collecting outdoor surface water as a main source of water. In Mirap, increased AOR overlapped with proximity to densely vegetated areas of the village. The identification of household and environmental factors associated with increased spatial risk may serve as useful indicators of transmission hotspots and inform the development of tailored approaches for malaria control.

BackgroundRegression modeling methods are commonly used to estimate influenza‐associated mortality using covariates such as laboratory‐confirmed influenza activity in the population as a proxy of influenza incidence.ObjectiveWe examined the choices of influenza proxies that can be used from influenza laboratory surveillance data and their impact on influenza‐associated mortality estimates.MethodSemiparametric generalized additive models with a smoothing spline were applied on national mortality data from South Africa and influenza surveillance data as covariates to obtain influenza‐associated mortality estimates from respiratory causes from 2009 to 2013. Proxies examined included alternative ways of expressing influenza laboratory surveillance data such as weekly or yearly proportion or rate of positive samples, using influenza subtypes, or total influenza data and expressing the data as influenza season‐specific or across all seasons.ResultBased on model fit, weekly proportion and influenza subtype‐specific proxy formulation provided the best fit. The choice of proxies used gave large differences to mortality estimates, but the 95% confidence interval of these estimates overlaps.ConclusionRegardless of proxy chosen, mortality estimates produced may be broadly consistent and not statistically significant for public health practice.

Parasitology and parasitic infections / International Journal of Infectious Diseases 101(S1) (2021) 419-436 thin blood smear pictures. This tool provides vital size/shape features, such as width, length and the perimeter of the T. cruzi and helps to attain a classification accuracy of 91.06% with the SVM classifier.Conclusion: This work proposes a soft-computing tool to detect, analyze and classify the T. cruzi existing in the thin blood smear microscopic images. This work achieved a classification accuracy of 91.06%, which confirms that the proposed tool has high clinical significance and it can be used for automated diagnosis of T. cruzi.