Forecasting Human African Trypanosomiasis Prevalences from Population Screening Data Using Continuous Time Models.

To eliminate and eradicate gambiense human African trypanosomiasis (HAT), maximizing the effectiveness of active case finding is of key importance. The progression of the epidemic is largely influenced by the planning of these operations. This paper introduces and analyzes five models for predicting HAT prevalence in a given village based on past observed prevalence levels and past screening activities in that village. Based on the quality of prevalence level predictions in 143 villages in Kwamouth (DRC), and based on the theoretical foundation underlying the models, we consider variants of the Logistic Model-a model inspired by the SIS epidemic model-to be most suitable for predicting HAT prevalence levels. Furthermore, we demonstrate the applicability of this model to predict the effects of planning policies for screening operations. Our analysis yields an analytical expression for the screening frequency required to reach eradication (zero prevalence) and a simple approach for determining the frequency required to reach elimination within a given time frame (one case per 10000). Furthermore, the model predictions suggest that annual screening is only expected to lead to eradication if at least half of the cases are detected during the screening rounds. This paper extends knowledge on control strategies for HAT and serves as a basis for further modeling and optimization studies.

Process modularity, supply chain responsiveness, and moderators: The Médecins Sans Frontières response to the Covid-19 pandemic.

The unprecedented scale of the Covid-19 pandemic has been a challenge for health supply chains around the world. Many international humanitarian organizations have had to ensure the continuity of their already complex development programs, while addressing their supply chain disruptions linked to the pandemic. Process modularity has frequently been advocated as a strategy to mitigate such disruptions, although empirical evidence regarding its impact on supply chain responsiveness and what moderates this impact is scarce. This exploratory research uses supply chain data analysis, qualitative content analysis, interviews, and a three-round Delphi study to investigate how Doctors without Borders (Médecins Sans Frontières; MSF) and its 151 missions employed process modularity during the Covid-19 pandemic. Our results show that despite severe disruptions, process modularity-based on a modular architecture, interfaces, and standards-has helped MSF maintain supply chain responsiveness. Specifically, it (1) enabled time-consuming, nonessential tasks to be skipped, (2) relieved internal and external bottlenecks, and (3) facilitated better allocation and prioritization. Our analyses also put forward eight moderators, structured in three dimensions (visibility, alignment, and resource orchestration), which can affect the impact of process modularity on supply chain responsiveness. We extend the literature on supply chain responsiveness and process modularity by presenting extensive empirical results suggesting that process modularity improves responsiveness in crisis situations, how it does so, and what moderates this impact. Our study thereby highlights the potential of this strategy and provides operationally relevant insights that could help organizations to implement or to review and redesign their process modularity.