Caroline O. Buckee

A bstract: Background: Extreme heat is an increasingly pressing public health threat, particularly in South Asia. Yet the temperatures at which mortality risk begins to increase in Indian cities remain poorly characterized. Operational heat wave definitions are based on meteorological criteria and may not reflect epidemiologically relevant temperature thresholds. Widely used distributed lag non-linear models are designed to flexibly estimate expected mortality as a function of temperature, but do not directly target the onset of excess risk from heat, a quantity central to early warning and planning. Methods: We conducted a multi-city time-series study of daily mortality and temperature data in Ahmedabad (2001–2010), Chennai (2012–2017), Delhi (2011–2015), Kolkata (2011–2018), and Mumbai (2011–2015). We developed a data-driven structured modeling framework that permits likelihood-based inference for city-specific temperatures at which heat-related mortality first exceeds seasonal background levels, a parameter that is challenging to estimate and perform statistical inference from short and noisy series. Using these health-based thresholds, we also estimated heat-associated excess deaths. Results: In all five cities, the onset of excess mortality occurred at temperatures substantially lower than those implied by current operational heat wave definitions. The proposed framework yielded stable and interpretable threshold estimates and confidence intervals across heterogeneous climates and time-series lengths and identified a larger burden of heat-associated mortality than would be captured using standard meteorological criteria. Conclusions: Data-driven estimation of mortality-based heat thresholds provides epidemiologically relevant targets for early warning systems and heat action plans. Reliance on meteorological definitions alone may delay intervention and underestimate population risk in rapidly warming regions. Key messages: We estimated the temperature at which mortality risk begins to increase above seasonal background levels in warm-climate settings and compared these estimates with current operational heat wave definitions in five Indian cities. Using a structured modeling approach motivated by observed patterns in the data, we found that mortality risk began to increase at temperatures below current operational heat wave thresholds, with higher-intensity heat events associated with a larger share of excess mortality. These findings suggest that epidemiologically derived thresholds may complement existing meteorological definitions in characterizing heat-related risk and setting triggers for action plans and warning systems.

Before 2026, Bundibugyo virus disease had only two recognized outbreaks. By August 2026, delayed recognition and multi-day sample transport in eastern Democratic Republic of the Congo had contributed to a large, spatially dispersed epidemic. We hypothesized that model-guided placement of decentralized diagnostics in frontier health zones could help contain spatial spread by acting as a spatial fence: delayed recognition would be converted into timely polymerase chain reaction (PCR) confirmation, isolation, and contact tracing before imported lineages became established. We fit a Bayesian spatial branching process to public situation reports through 2 August 2026, with Flowminder movement and a latent-burden anchor. Lineages had overdispersed offspring, PCR access and turnaround, clinical recognition, isolation, tracing, safe burial, and local behaviour change. Because the observed history did not identify future control, we assumed a national response ramp yielding West-Africa-scale status-quo burden and replayed 2,000 posterior trees with 5-100 zone-level PCR laboratories, deployment delays of 0-180 days, and postmortem rapid diagnostic test (RDT) coverage. The posterior reproduced case, death, latent-burden, and spatial targets. Speed dominated: 35 PCR laboratories averted a median 49% of future deaths if deployed immediately, 38% after 14 days, and 11% after 60 days, with 90% prediction intervals of 30-65%, 23-53%, and 5-20%, respectively. Placement also mattered: with a 30-day delay, model-informed placement averted 18-33% of future deaths with 20-100 laboratories, versus 1-8% under random placement. Postmortem RDTs partly filled gaps left by live-case detection, increasing future deaths averted from 26% to 41% with 20 laboratories, a 14-day delay, and 75% coverage. Decentralized diagnostics can slow spatial spread when they are placed ahead of likely importations and paired with staffing, isolation, tracing, and safe-burial capacity.

Background: Malaria transmission in the Chittagong Hill Tracts (CHT) districts in Bangladesh is characterized by considerable heterogeneity in incidence and the frequent mixing and importation of parasites across districts. Thus, elimination efforts must account for human mobility between endemic and non-endemic locations, and the relative importance of local transmission and parasite importation domestically. Methods: We construct a metapopulation malaria model, parameterized by human mobility data and fit to epidemiological data, to guide elimination efforts in the region. Results: We find substantial heterogeneity in the transmission intensity across the CHT, with the estimated basic reproduction number varying greatly across places with similar levels of observed incidence. When vector control interventions are applied locally, the greatest impact in reducing overall incidence are in places with both high transmission intensity and high connectivity with more populated districts in the western part of the CHT. Conclusions: Local elimination in several areas with low or intermediate incidence has a moderate impact in reducing overall incidence, indicating that only focusing on high incidence areas is not sufficient for malaria elimination. More generally, our modeling framework can be used to prioritize resource allocation and identify the conditions necessary for malaria elimination in the region.

Variation in malaria infection risk, a product of disease exposure and immunity, is poorly understood. We genotypically profiled over 13,000 blood samples from a six-year longitudinal cohort in Mali to characterize malaria infection dynamics with detail. We generated Plasmodium falciparum amplicon sequencing data from 464 participants (aged 3 months – 25 years) across the six-month 2011 transmission season and profiled a subset of 120 participants across the subsequent five annual transmission seasons. We measured infection rate as the molecular force of infection (molFOI, number of genetically distinct parasites acquired over time). We found that molFOI varied extensively among individuals (0–55 in 2011) but was independent of age and consistent within individuals over multiple seasons. Reported bednet usage was nearly universal. The HbS allele was associated with lower molFOI, and functional antibody signatures for the CSP C-term and RH5 antigens were correlated with low molFOI participants, identifying candidate immune correlates of protection. The large inter-individual variability in molFOI and consistency of intra-individual infection rate over time exhibits much greater dynamic range than malaria case incidence, and is most likely due to heterogeneous exposure to infectious mosquito bites. This and other factors contributing to variable infection risk should be considered in future clinical trials and implementation of malaria interventions.

Objective/Background: Transmission-dynamic models are commonly used to study infectious disease epidemiology. Calibration involves identifying model parameter values that align model outputs with observed data or other evidence. Inaccurate calibration and inconsistent reporting produce inference errors and limit reproducibility, compromising confidence in modeled results. No standardized framework exists for reporting on calibration of infectious disease models, and an understanding of current calibration approaches is lacking. Methods: We developed a 15-item framework for reporting calibration practices and applied it in a scoping review to assess calibration approaches and evaluate reporting comprehensiveness in transmission-dynamic models of tuberculosis, HIV and malaria published between January 1, 2018, and January 16, 2024. We searched relevant databases and websites to identify eligible publications, including peer-reviewed studies where these models were calibrated to empirical data or published estimates. Results: We identified 411 eligible studies encompassing 419 models, with 74% (n=309) being compartmental models and 20% (n=82) individual-based models (IBMs). The predominant analytical purpose was to evaluate interventions (71% of models, n=298). Parameters were calibrated mainly because they were unknown or ambiguous (40%, n=168), or because determining their value was relevant to the scientific question beyond being necessary to run the model (20%, n=85). The choice of calibration method was significantly associated with model structure (p-value<0.001) and stochasticity (p-value=0.006), with approximate Bayesian computation more frequently used with IBMs and Markov-Chain Monte Carlo with compartmental models. Regarding reporting comprehensiveness, all 15 framework items in the framework were reported in 4% (n=18) of models; 11-14 items in 66% (n=277), and 10 or fewer items in 28% (n= 124). Implementation code was the least reported, available in only 20% (n=82) of models. Conclusions: Reporting on calibration is heterogeneous in recent infectious disease modeling literature. Our proposed framework for reporting of calibration approaches could support improved reproducibility and credibility of modeled analyses. Author Summary: Calibration, the identification of parameter values so that model outcomes are consistent with observed data or other evidence, is often employed in the process of obtaining model results to inform health decision making. Despite its importance, there has not been a standardized framework for reporting how calibration is conducted in infectious disease modeling studies. This has led to inconsistent reporting practices and challenges in reproducing model results, potentially compromising confidence in their validity. We developed a calibration reporting framework, based on best practices found in the literature and informed by our expertise in conducting calibration. To assess calibration practices and their reporting, we applied our framework in a scoping review of 419 infectious disease transmission models of HIV, TB and malaria published between 2018 and 2024. Most models reviewed were compartmental (74%) or individual-based (20%), and the choice of calibration methods was associated with model structure and stochasticity. Calibration was conducted predominantly in the context of models aimed at evaluating the impact of disease control interventions, highlighting the role of calibration in decision making. Parameters were calibrated mainly because they were unknown or ambiguous, or because reporting their value was relevant to the scientific question beyond just being necessary to run the model. The comprehensiveness of calibration reporting varied across models, with most models omitting 1 to 5 items in the framework. Accessible implementation code was the most underreported, with only 20% of models including it. Our proposed framework could serve as a tool to standardize calibration reporting, thereby enhancing the transparency and reproducibility of calibration processes in transmission-dynamic models.

Background: Digital data sources such as mobile phone call detail records (CDRs) are increasingly being used to estimate population mobility fluxes and to predict the spatiotemporal dynamics of infectious disease outbreaks. Differences in mobile phone operators’ geographic coverage, however, may result in biased mobility estimates. Methods: We leverage a unique dataset consisting of CDRs from three mobile phone operators in Bangladesh and digital trace data from Meta’s Data for Good program to compare mobility patterns across these sources. We use a metapopulation model to compare the sources’ effects on simulated outbreak trajectories, and compare results with a benchmark model with data from all three operators, representing around 100 million subscribers across the country. Results: We show that mobility sources can vary significantly in their coverage of travel routes and geographic mobility patterns. Differences in projected outbreak dynamics are more pronounced at finer spatial scales, especially if the outbreak is seeded in smaller and/or geographically isolated regions. In some instances, a simple diffusion (gravity) model was better able to capture the timing and spatial spread of the outbreak compared to the sparser mobility sources. Conclusions: Our results highlight the potential biases in predicted outbreak dynamics from a metapopulation model parameterized with non-population representative data, and the limits to the generalizability of models built on these types of novel human behavioral data.

The malaria parasites Plasmodium falciparum and Plasmodium vivax differ in key biological processes and associated clinical effects, but consequences on population-level transmission dynamics are difficult to predict. This co-endemic malaria study from Guyana details important epidemiological contrasts between the species by coupling population genomics (1396 spatiotemporally matched parasite genomes, primarily from 2020–21) with sociodemographic analysis (nationwide patient census from 2019). We describe how P. falciparum forms large, interrelated subpopulations that sporadically expand but generally exhibit restrained dispersal, whereby spatial distance and patient travel statistics predict parasite identity-by-descent (IBD). Case bias towards working-age adults is also strongly pronounced. P. vivax exhibits 46% higher average nucleotide diversity (π) and 6.5x lower average IBD. It occupies a wider geographic range, without evidence for outbreak-like expansions, only microgeographic patterns of isolation-by-distance, and weaker case bias towards adults. Possible latency-relapse effects also manifest in various analyses. For example, 11.0% of patients diagnosed with P. vivax in Greater Georgetown report no recent travel to endemic zones, and P. vivax clones recur in 11 of 46 patients incidentally sampled twice during the study. Polyclonality rate is also 2.1x higher than in P. falciparum, does not trend positively with estimated incidence, and correlates uniquely to selected demographics. We discuss possible underlying mechanisms and implications for malaria control.

Introduction: Long-term COVID-19 complications are a globally pervasive threat, but their social determinants are often understudied relative to clinical risk factors. Thus, the role that clinical comorbidities play in determining disparate long COVID outcomes across socio-demographic groups is relatively unknown. Here, we ranked social and clinical predictors of quality of life (QoL) with long COVID and measured the extent to which clinical intermediates explain any observed relationships between social factors and long COVID QoL. Methods: We used demographic, comorbidity, treatment, and quality of life data from acute case reporting forms and follow-up surveys collected for a multinational prospective cohort study, with focus on data from Norway (n=1,672), the UK (n=1,064), and Russia (n=1,155). We considered the social factors employment status, educational attainment, and female sex and defined our primary outcome as QoL utility scores among subjects reporting any long COVID-associated symptoms. Results: We found that, in addition to age, neuropsychological, and rheumatological comorbidities, educational attainment, employment status, and sex were consistently identified as top predictors of long COVID-associated QoL utility scores. Furthermore, 98.2% (95% CI: 87.3%, 100%) and 89.4% (74.6%, 99.6%) of the positive adjusted associations between high educational attainment or full-time employment and long COVID QoL utility scores remained unexplained by key long COVID-predicting comorbidities in Norway and the UK. The same was true for 84.2% (46%, 100%), 67.6% (45.5%, 89.7%), and 87.2% (71.3%, 100%) of the negative adjusted associations between female sex and long COVID QoL utility scores in Norway, the UK, and Russia. Conclusion: Socio-economic proxies and sex are strong predictors of long COVID QoL independent of commonly emphasized comorbidity pathways and warrant increased attention in interventions focused on mitigating long COVID burden. Long COVID management efforts that target social determinants should be tailored to a specific country context, given the heterogeneity in findings observed across settings. What is already known on this topic: Clinical comorbidities like asthma, chronic cardiac disease, and diabetes, are well-established long COVID risk factors; social factors, namely socioeconomic disadvantage and gender, have also been identified as important sources of long COVID risk; the relative rankings of these factors in differentiating quality of life with long COVID is less recognized Furthermore, key clinical predisposing factors have been shown to play a minimal role in confounding observed social disparities in long COVID risk; their potential role as explanatory intermediates is generally underexplored What this study adds: Evaluates both social and clinical factors (individually and grouped by shared mechanisms) as predictors of long COVID-associated quality of life, an outcome measure that can capture a gradient in experiences with the condition Importantly, subverts the commonplace emphasis on differences in comorbidity burden explaining observed disparities in health outcomes by quantifying the proportion of relationships between different social factors and long COVID quality of life that are unexplained by clinical intermediates, applying flexible statistical mediation approaches How this study might affect research, practice or policy: Motivates broadening the focus of long COVID prevention efforts to look beyond simply targeting clinical intermediates as a way to fully resolve social disparities and instead also explore the role of other upstream societal sources of disadvantage, for example, access to public health resources and clinical services or discrimintion

Plasmodium parasites, the causal agents of malaria, are eukaryotic organisms that obligately undergo sexual recombination within mosquitoes. However, in low transmission settings where most mosquitoes become infected with only a single parasite clone, parasites recombine with themselves, and the clonal lineage is propagated rather than broken up by outcrossing. We investigated whether stochastic/neutral factors drive the persistence and abundance of Plasmodium falciparum clonal lineages in Guyana, a country with relatively low malaria transmission, but the only setting in the Americas in which an important artemisinin resistance mutation ( pfk13 C580Y) has been observed. To investigate whether this clonality was potentially associated with the persistence and spatial spread of the mutation, we performed whole genome sequencing on 1,727 Plasmodium falciparum samples collected from infected patients across a five-year period (2016- 2021). We characterized the relatedness between each pair of monoclonal infections (n=1,409) through estimation of identity by descent (IBD) and also typed each sample for known or candidate drug resistance mutations. A total of 160 clones (mean IBD ≥ 0.90) were circulating in Guyana during the study period, comprising 13 highly related clusters (mean IBD ≥ 0.40). In the five-year study period, we observed a decrease in frequency of a mutation associated with artemisinin partner drug (piperaquine) resistance ( pfcrt C350R) and limited co-occurence of pfcrt C350R with duplications of plasmepsin 2/3 , an epistatic interaction associated with piperaquine resistance. We additionally report polymorphisms exhibiting evidence of selection for drug resistance or other phenotypes and reported a novel pfk13 mutation ( G718S ) as well as 61 nonsynonymous substitutions that increased markedly in frequency. However, P. falciparum clonal dynamics in Guyana appear to be largely driven by stochastic factors, in contrast to other geographic regions. The use of multiple artemisinin combination therapies in Guyana may have contributed to the disappearance of the pfk13 C580Y mutation. Author Summary: Malaria is caused by eukaryotic Plasmodium parasites, which undergo sexual recombination within mosquitoes. In settings with low transmission, such as Guyana, these parasites often recombine with themselves, leading to the propagation of identical clones. We explored the population genomics of Plasmodium falciparum malaria parasites in Guyana over five years to characterize clonal transmission dynamics and understand whether they were influenced by local drug resistance mutations under strong selection, including pfk13 C580Y, which confers resistance to artemisinin, and pfcrt C350R, which confers resistance to piperaquine. Using whole genome sequencing on 1,463 samples, we identified 160 clones, in which all parasites share at least 90% of their genomes through recent common ancestry. We observed a decrease in frequency of the pfcrt C350R mutation, as well as the disappearance of pfk13 C580Y. Our findings contrast with the deterministic rise of drug resistance mutations observed in other geographic regions, sometimes associated with clonality. The simultaneous use of at least two different artemisinin combination therapies may have prevented the spread of an artemisinin-resistant clone in Guyana, suggesting a strategy for resistance management in other geographic regions.

Background: Mathematical modeling of infectious diseases is an important decision-making tool for outbreak control. However, in Africa, limited expertise reduces the use and impact of these tools on policy. Therefore, there is a need to build capacity in Africa for the use of mathematical modeling to inform policy. Here we describe our experience implementing a mathematical modeling training program for public health professionals in East Africa. Methods: We used a deliverable-driven and learning-by-doing model to introduce trainees to the mathematical modeling of infectious diseases. The training comprised two two-week in-person sessions and a practicum where trainees received intensive mentorship. Trainees evaluated the content and structure of the course at the end of each week, and this feedback informed the strategy for subsequent weeks. Findings: Out of 875 applications from 38 countries, we selected ten trainees from three countries – Rwanda (6), Kenya (2), and Uganda (2) – with guidance from an advisory committee. Nine trainees were based at government institutions and one at an academic organization. Participants gained skills in developing models to answer questions of interest and critically appraising modeling studies. At the end of the training, trainees prepared policy briefs summarizing their modeling study findings. These were presented at a dissemination event to policymakers, researchers, and program managers. All trainees indicated they would recommend the course to colleagues and rated the quality of the training with a median score of 9/10. Conclusions: Mathematical modeling training programs for public health professionals in Africa can be an effective tool for research capacity building and policy support to mitigate infectious disease burden and forecast resources. Overall, the course was successful, owing to a combination of factors, including institutional support, trainees’ commitment, intensive mentorship, a diverse trainee pool, and regular evaluations.