Assessing the spatiotemporal malaria transmission intensity with heterogeneous risk factors:A modeling study in Cambodia

Malaria control can significantly benefit from a holistic and precise way of quantitatively measuring the transmission intensity,which needs to incorporate spatiotemporally varying risk factors.In this study,we conduct a systematic investigation to characterize malaria transmission intensity by taking a spatiotemporal network perspective,where nodes capture the local transmission intensities resulting from dominant vector species,the population density,and land cover,and edges describe the cross-region human mobility patterns.The inferred network enables us to accurately assess the transmission intensity over time and space from available empirical observations.Our study focuses on malaria-severe districts in Cambodia.The malaria transmission intensities determined using our transmission network reveal both qualitatively and quantitatively their seasonal and geographical characteristics:the risks increase in the rainy season and decrease in the dry season;remote and sparsely populated areas generally show higher transmission intensities than other areas.Our findings suggest that:the human mobility(e.g.,in planting/harvest seasons),environment(e.g.,temperature),and contact risk(coexistences of human and vector occurrence)contribute to malaria transmission in spatiotemporally varying degrees;quantitative relationships between these influential factors and the resulting malaria transmission risk can inform evidence-based tailor-made responses at the right locations and times.

Unreliability Tracing of Power Systems for Identifying the Most Critical Risk Factors Considering Mixed Uncertainties in Wind Power Output

For conventional power systems,the forced outage of components is the major cause of load shedding.Unreliability tracing is utilized to allocate the total system load-shedding risk among individual components in accordance with their different contributions.Therefore,critical components are identified and pertinent measures can be taken to improve system reliability.The integration of wind power introduces additional risk factors into power systems,causing previous unreliability tracing methods to become inapplicable.In this paper,a novel unreliability tracing method is proposed that considers both aleatory and epistemic uncertainties in wind power output and their impacts on power system load-shedding risk.First,modelling methods for wind power output considering aleatory and epistemic uncertainties and component outages are proposed.Then,a variance-based index is proposed to measure the contributions of individual risk factors to the system load-shedding risk.Finally,a novel unreliability tracing framework is developed to identify the critical factors that affect power system reliability.Case studies verify the ability of the proposed method to accurately allocate load-shedding risk to individual risk factors,thus providing decision support for reliability enhancement.

Contribution of high risk groups’unmet needs may be underestimated in epidemic models without risk turnover:A mechanistic modelling analysis

Background:Epidemic models of sexually transmitted infections(STIs)are often used to characterize the contribution of risk groups to overall transmission by projecting the transmission population attributable fraction(tPAF)of unmet prevention and treatment needs within risk groups.However,evidence suggests that STI risk is dynamic over an individual’s sexual life course,which manifests as turnover between risk groups.We sought to examine the mechanisms by which turnover influences modelled projections of the tPAF of high risk groups.Methods:We developed a unifying,data-guided framework to simulate risk group turnover in deterministic,compartmental transmission models.We applied the framework to an illustrative model of an STI and examined the mechanisms by which risk group turnover influenced equilibrium prevalence across risk groups.We then fit a model with and without turnover to the same risk-stratified STI prevalence targets and compared the inferred level of risk heterogeneity and tPAF of the highest risk group projected by the two models.Results:The influence of turnover on group-specific prevalence was mediated by three main phenomena:movement of previously high risk individuals with the infection into lower risk groups;changes to herd effect in the highest risk group;and changes in the number of partnerships where transmission can occur.Faster turnover led to a smaller ratio of STI prevalence between the highest and lowest risk groups.Compared to the fitted model without turnover,the fitted model with turnover inferred greater risk heterogeneity and consistently projected a larger tPAF of the highest risk group over time.Implications:If turnover is not captured in epidemic models,the projected contribution of high risk groups,and thus,the potential impact of prioritizing interventions to address their needs,could be underestimated.To aid the next generation of tPAF models,data collection efforts to parameterize risk group turnover should be prioritized.