Burkitt lymphoma risk shows geographic and temporal associations with Plasmodium falciparum infections in Uganda, Tanzania, and Kenya.

Endemic Burkitt lymphoma (eBL) is a pediatric cancer coendemic with malaria in sub-Saharan Africa, suggesting an etiological link between them. However, previous cross-sectional studies of limited geographic areas have not found a convincing association. We used spatially detailed data from the Epidemiology of Burkitt Lymphoma in East African Children and Minors (EMBLEM) study to assess this relationship. EMBLEM is a case-control study of eBL from 2010 through 2016 in six regions of Kenya, Uganda, and Tanzania. To measure the intensity of exposure to the malaria parasite, Plasmodium falciparum, among children in these regions, we used high-resolution spatial data from the Malaria Atlas Project to estimate the annual number of P. falciparum infections from 2000 through 2016 for each of 49 districts within the study region. Cumulative P. falciparum exposure, calculated as the sum of annual infections by birth cohort, varied widely, with a median of 47 estimated infections per child by age 10, ranging from 4 to 315 infections. eBL incidence increased 39% for each 100 additional lifetime P. falciparum infections (95% CI: 6.10 to 81.04%) with the risk peaking among children aged 5 to 11 and declining thereafter. Alternative models using estimated annual P. falciparum infections 0 to 10 y before eBL onset were inconclusive, suggesting that eBL risk is a function of cumulative rather than recent cross-sectional exposure. Our findings provide population-level evidence that eBL is a phenotype related to heavy lifetime exposure to P. falciparum malaria and support emphasizing the link between malaria and eBL.

Invited Commentary: To Make Long-Term Gains Against Infection Inequity, Infectious Disease Epidemiology Needs to Develop a More Sociological Imagination.

In a recent article in the Journal, Noppert et al. (Am J Epidemiol. 2023;192(3):475-482) articulated in detail the mechanisms connecting high-level "fundamental social causes" of health inequity to inequitable infectious disease outcomes, including infection, severe disease, and death. In this commentary, we argue that while intensive focus on intervening mechanisms is welcome and necessary, it cannot occur in isolation from examination of the way that fundamental social causes-including racism, socioeconomic inequity, and social stigma-sustain infection inequities even when intervening mechanisms are addressed. We build on the taxonomy of intervening mechanisms laid out by Noppert et al. to create a road map for strengthening the connection between fundamental cause theory and infectious disease epidemiology and discuss its implications for future research and intervention.

There are no equal opportunity infectors: Epidemiological modelers must rethink our approach to inequality in infection risk.

Mathematical models have come to play a key role in global pandemic preparedness and outbreak response: helping to plan for disease burden, hospital capacity, and inform nonpharmaceutical interventions. Such models have played a pivotal role in the COVID-19 pandemic, with transmission models-and, by consequence, modelers-guiding global, national, and local responses to SARS-CoV-2. However, these models have largely not accounted for the social and structural factors, which lead to socioeconomic, racial, and geographic health disparities. In this piece, we raise and attempt to clarify several questions relating to this important gap in the research and practice of infectious disease modeling: Why do epidemiologic models of emerging infections typically ignore known structural drivers of disparate health outcomes? What have been the consequences of a framework focused primarily on aggregate outcomes on infection equity? What should be done to develop a more holistic approach to modeling-based decision-making during pandemics? In this review, we evaluate potential historical and political explanations for the exclusion of drivers of disparity in infectious disease models for emerging infections, which have often been characterized as "equal opportunity infectors" despite ample evidence to the contrary. We look to examples from other disease systems (HIV, STIs) and successes in including social inequity in models of acute infection transmission as a blueprint for how social connections, environmental, and structural factors can be integrated into a coherent, rigorous, and interpretable modeling framework. We conclude by outlining principles to guide modeling of emerging infections in ways that represent the causes of inequity in infection as central rather than peripheral mechanisms.

Optimizing laboratory-based surveillance networks for monitoring multi-genotype or multi-serotype infections.

With the aid of laboratory typing techniques, infectious disease surveillance networks have the opportunity to obtain powerful information on the emergence, circulation, and evolution of multiple genotypes, serotypes or other subtypes of pathogens, informing understanding of transmission dynamics and strategies for prevention and control. The volume of typing performed on clinical isolates is typically limited by its ability to inform clinical care, cost and logistical constraints, especially in comparison with the capacity to monitor clinical reports of disease occurrence, which remains the most widespread form of public health surveillance. Viewing clinical disease reports as arising from a latent mixture of pathogen subtypes, laboratory typing of a subset of clinical cases can provide inference on the proportion of clinical cases attributable to each subtype (i.e., the mixture components). Optimizing protocols for the selection of isolates for typing by weighting specific subpopulations, locations, time periods, or case characteristics (e.g., disease severity), may improve inference of the frequency and distribution of pathogen subtypes within and between populations. Here, we apply the Disease Surveillance Informatics Optimization and Simulation (DIOS) framework to simulate and optimize hand foot and mouth disease (HFMD) surveillance in a high-burden region of western China. We identify laboratory surveillance designs that significantly outperform the existing network: the optimal network reduced mean absolute error in estimated serotype-specific incidence rates by 14.1%; similarly, the optimal network for monitoring severe cases reduced mean absolute error in serotype-specific incidence rates by 13.3%. In both cases, the optimal network designs achieved improved inference without increasing subtyping effort. We demonstrate how the DIOS framework can be used to optimize surveillance networks by augmenting clinical diagnostic data with limited laboratory typing resources, while adapting to specific, local surveillance objectives and constraints.

Fine-scale spatial clustering of measles nonvaccination that increases outbreak potential is obscured by aggregated reporting data.

The United States experienced historically high numbers of measles cases in 2019, despite achieving national measles vaccination rates above the World Health Organization recommendation of 95% coverage with two doses. Since the COVID-19 pandemic began, resulting in suspension of many clinical preventive services, pediatric vaccination rates in the United States have fallen precipitously, dramatically increasing risk of measles resurgence. Previous research has shown that measles outbreaks in high-coverage contexts are driven by spatial clustering of nonvaccination, which decreases local immunity below the herd immunity threshold. However, little is known about how to best conduct surveillance and target interventions to detect and address these high-risk areas, and most vaccination data are reported at the state-level-a resolution too coarse to detect community-level clustering of nonvaccination characteristic of recent outbreaks. In this paper, we perform a series of computational experiments to assess the impact of clustered nonvaccination on outbreak potential and magnitude of bias in predicting disease risk posed by measuring vaccination rates at coarse spatial scales. We find that, when nonvaccination is locally clustered, reporting aggregate data at the state- or county-level can result in substantial underestimates of outbreak risk. The COVID-19 pandemic has shone a bright light on the weaknesses in US infectious disease surveillance and a broader gap in our understanding of how to best use detailed spatial data to interrupt and control infectious disease transmission. Our research clearly outlines that finer-scale vaccination data should be collected to prevent a return to endemic measles transmission in the United States.

The Role of Staff in Transmission of SARS-CoV-2 in Long-term Care Facilities.

BACKGROUND: US long-term care facilities (LTCFs) have experienced a disproportionate burden of COVID-19 morbidity and mortality. METHODS: We examined SARS-CoV-2 transmission among residents and staff in 60 LTCFs in Fulton County, Georgia, from March 2020 to September 2021. Using the Wallinga-Teunis method to estimate the time-varying reproduction number, R(t), and linear-mixed regression models, we examined associations between case characteristics and R(t). RESULTS: Case counts, outbreak size and duration, and R(t) declined rapidly and remained low after vaccines were first distributed to LTCFs in December 2020, despite increases in community incidence in summer 2021. Staff cases were more infectious than resident cases (average individual reproduction number, R i = 0.6 [95% confidence intervals [CI] = 0.4, 0.7] and 0.1 [95% CI = 0.1, 0.2], respectively). Unvaccinated resident cases were more infectious than vaccinated resident cases (R i = 0.5 [95% CI = 0.4, 0.6] and 0.2 [95% CI = 0.0, 0.8], respectively), but estimates were imprecise. CONCLUSIONS: COVID-19 vaccines slowed transmission and contributed to reduced caseload in LTCFs. However, due to data limitations, we were unable to determine whether breakthrough vaccinated cases were less infectious than unvaccinated cases. Staff cases were six times more infectious than resident cases, consistent with the hypothesis that staff were the primary drivers of SARS-CoV-2 transmission in LTCFs.

Protective impacts of household-based tuberculosis contact tracing are robust across endemic incidence levels and community contact patterns.

There is an emerging consensus that achieving global tuberculosis control targets will require more proactive case finding approaches than are currently used in high-incidence settings. Household contact tracing (HHCT), for which households of newly diagnosed cases are actively screened for additional infected individuals is a potentially efficient approach to finding new cases of tuberculosis, however randomized trials assessing the population-level effects of such interventions in settings with sustained community transmission have shown mixed results. One potential explanation for this is that household transmission is responsible for a variable proportion of population-level tuberculosis burden between settings. For example, transmission is more likely to occur in households in settings with a lower tuberculosis burden and where individuals mix preferentially in local areas, compared with settings with higher disease burden and more dispersed mixing. To better understand the relationship between endemic incidence levels, social mixing, and the impact of HHCT, we developed a spatially explicit model of coupled household and community transmission. We found that the impact of HHCT was robust across settings of varied incidence and community contact patterns. In contrast, we found that the effects of community contact tracing interventions were sensitive to community contact patterns. Our results suggest that the protective benefits of HHCT are robust and the benefits of this intervention are likely to be maintained across epidemiological settings.

Pneumonia Following Symptomatic Influenza Infection Among Nicaraguan Children Before and After Introduction of the Pneumococcal Conjugate Vaccine.

Influenza is associated with primary viral and secondary bacterial pneumonias; however, the dynamics of this relationship in populations with varied levels of pneumococcal vaccination remain unclear. We conducted nested matched case-control studies in 2 prospective cohorts of Nicaraguan children aged 2-14 years: 1 before pneumococcal conjugate vaccine introduction (2008-2010) and 1 following introduction and near universal adoption (2011-2018). The association between influenza and pneumonia was similar in both cohorts. Participants with influenza (across types/subtypes) had higher odds of developing pneumonia in the month following influenza infection. These findings underscore the importance of considering influenza in interventions to reduce global pneumonia burden.

Racial Disparities in Coronavirus Disease 2019 (COVID-19) Mortality Are Driven by Unequal Infection Risks.

BACKGROUND: As of 1 November 2020, there have been >230 000 deaths and 9 million confirmed and probable cases attributable to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in the United States. However, this overwhelming toll has not been distributed equally, with geographic, race/ethnic, age, and socioeconomic disparities in exposure and mortality defining features of the US coronavirus disease 2019 (COVID-19) epidemic. METHODS: We used individual-level COVID-19 incidence and mortality data from the state of Michigan to estimate age-specific incidence and mortality rates by race/ethnic group. Data were analyzed using hierarchical Bayesian regression models, and model results were validated using posterior predictive checks. RESULTS: In crude and age-standardized analyses we found rates of incidence and mortality more than twice as high than for Whites for all groups except Native Americans. Blacks experienced the greatest burden of confirmed and probable COVID-19 (age-standardized incidence, 1626/100 000 population) and mortality (age-standardized mortality rate, 244/100 000). These rates reflect large disparities, as Blacks experienced age-standardized incidence and mortality rates 5.5 (95% posterior credible interval [CrI], 5.4-5.6) and 6.7 (95% CrI, 6.4-7.1) times higher than Whites, respectively. We found that the bulk of the disparity in mortality between Blacks and Whites is driven by dramatically higher rates of COVID-19 infection across all age groups, particularly among older adults, rather than age-specific variation in case-fatality rates. CONCLUSIONS: This work suggests that well-documented racial disparities in COVID-19 mortality in hard-hit settings, such as Michigan, are driven primarily by variation in household, community, and workplace exposure rather than case-fatality rates.

Individual-level Association of Influenza Infection With Subsequent Pneumonia: A Case-control and Prospective Cohort Study.

BACKGROUND: Pneumonia is a leading cause of mortality worldwide. Influenza may result in primary pneumonia or be associated with secondary bacterial pneumonia. While the association with secondary pneumonia has been established ecologically, individual-level evidence remains sparse and the risk period for pneumonia following influenza poorly defined. METHODS: We conducted a matched case-control study and a prospective cohort study among Nicaraguan children aged 0-14 years from 2011 through 2018. Physicians diagnosed pneumonia cases based on Integrated Management for Childhood Illness guidelines. Cases were matched with up to 4 controls on age (months) and study week. We fit conditional logistic regression models to assess the association between influenza subtype and subsequent pneumonia development, and a Bayesian nonlinear survival model to estimate pneumonia hazard following influenza. RESULTS: Participants with influenza had greater risk of developing pneumonia in the 30 days following onset compared to those without influenza (matched odds ratio [mOR], 2.7 [95% confidence interval {CI}, 1.9-3.9]). Odds of developing pneumonia were highest for participants following A(H1N1)pdm09 illness (mOR, 3.7 [95% CI, 2.0-6.9]), followed by influenza B and A(H3N2). Participants' odds of pneumonia following influenza were not constant, showing distinct peaks 0-6 days (mOR, 8.3 [95% CI, 4.8-14.5] days) and 14-20 (mOR, 2.5 [95% CI, 1.1-5.5] days) after influenza infection. CONCLUSIONS: Influenza is a significant driver of both primary and secondary pneumonia among children. The presence of distinct periods of elevated pneumonia risk in the 30 days following influenza supports multiple etiological pathways.

Coccidioidomycosis and COVID-19 Co-Infection, United States, 2020.

We review the interaction between coronavirus disease (COVID-19) and coccidioidomycosis, a respiratory infection caused by inhalation of Coccidioides fungal spores in dust. We examine risk for co-infection among construction and agricultural workers, incarcerated persons, Black and Latino populations, and persons living in high dust areas. We further identify common risk factors for co-infection, including older age, diabetes, immunosuppression, racial or ethnic minority status, and smoking. Because these diseases cause similar symptoms, the COVID-19 pandemic might exacerbate delays in coccidioidomycosis diagnosis, potentially interfering with prompt administration of antifungal therapies. Finally, we examine the clinical implications of co-infection, including severe COVID-19 and reactivation of latent coccidioidomycosis. Physicians should consider coccidioidomycosis as a possible diagnosis when treating patients with respiratory symptoms. Preventive measures such as wearing face masks might mitigate exposure to dust and severe acute respiratory syndrome coronavirus 2, thereby protecting against both infections.

Measuring Multiple Dimensions and Indices of Nonvaccination Clustering in Michigan, 2008-2018.

Michigan experienced a significant measles outbreak in 2019 amidst rising rates of nonmedical vaccine exemptions (NMEs) and low vaccination coverage compared with the rest of the United States. There is a critical need to better understand the landscape of nonvaccination in Michigan to assess the risk of vaccine-preventable disease outbreaks in the state, yet there is no agreed-upon best practice for characterizing spatial clustering of nonvaccination, and numerous clustering metrics are available in the statistical, geographical, and epidemiologic literature. We used school-level data to characterize the spatiotemporal landscape of vaccine exemptions in Michigan for the period 2008-2018 using Moran's I, the isolation index, the modified aggregation index, and the Theil index at 4 spatial scales. We also used nonvaccination thresholds of 5%, 10%, and 20% to assess the bias incurred when aggregating vaccination data. We found that aggregating school-level data to levels commonly used for public reporting can lead to large biases in identifying the number and location of at-risk students and that different clustering metrics yielded variable interpretations of the nonvaccination landscape in Michigan. This study shows the importance of choosing clustering metrics with their mechanistic interpretations in mind, be it large- or fine-scale heterogeneity or between- and within-group contributions to spatial variation.

Measuring the impact of spatial perturbations on the relationship between data privacy and validity of descriptive statistics.

BACKGROUND: Like many scientific fields, epidemiology is addressing issues of research reproducibility. Spatial epidemiology, which often uses the inherently identifiable variable of participant address, must balance reproducibility with participant privacy. In this study, we assess the impact of several different data perturbation methods on key spatial statistics and patient privacy. METHODS: We analyzed the impact of perturbation on spatial patterns in the full set of address-level mortality data from Lawrence, MA during the period from 1911 to 1913. The original death locations were perturbed using seven different published approaches to stochastic and deterministic spatial data anonymization. Key spatial descriptive statistics were calculated for each perturbation, including changes in spatial pattern center, Global Moran's I, Local Moran's I, distance to the k-th nearest neighbors, and the L-function (a normalized form of Ripley's K). A spatially adapted form of k-anonymity was used to measure the privacy protection conferred by each method, and its compliance with HIPAA and GDPR privacy standards. RESULTS: Random perturbation at 50 m, donut masking between 5 and 50 m, and Voronoi masking maintain the validity of descriptive spatial statistics better than other perturbations. Grid center masking with both 100 × 100 and 250 × 250 m cells led to large changes in descriptive spatial statistics. None of the perturbation methods adhered to the HIPAA standard that all points have a k-anonymity > 10. All other perturbation methods employed had at least 265 points, or over 6%, not adhering to the HIPAA standard. CONCLUSIONS: Using the set of published perturbation methods applied in this analysis, HIPAA and GDPR compliant de-identification was not compatible with maintaining key spatial patterns as measured by our chosen summary statistics. Further research should investigate alternate methods to balancing tradeoffs between spatial data privacy and preservation of key patterns in public health data that are of scientific and medical importance.

Understanding the Importance of Contact Heterogeneity and Variable Infectiousness in the Dynamics of a Large Norovirus Outbreak.

BACKGROUND: Large norovirus (NoV) outbreaks are explosive in nature and vary widely in final size and duration, suggesting that superspreading combined with heterogeneous contact may explain these dynamics. Modeling tools that can capture heterogeneity in infectiousness and contact are important for NoV outbreak prevention and control, yet they remain limited. METHODS: Data from a large NoV outbreak at a Dutch scout jamboree, which resulted in illness among 326 (of 4500 total) individuals from 7 separate camps, were used to examine the contributions of individual variation in infectiousness and clustered contact patterns to the transmission dynamics. A Bayesian hierarchical model of heterogeneous, clustered outbreak transmission was applied to represent (1) between-individual heterogeneity in infectiousness and (2) heterogeneous patterns of contact. RESULTS: We found wide heterogeneity in infectiousness across individuals, suggestive of superspreading. Nearly 50% of individual infectiousness was concentrated in the individual's subcamp of residence, with the remainder distributed over other subcamps. This suggests a source-and-sink dynamic in which subcamps with greater average infectiousness fed cases to those with a lower transmission rate. Although the per capita transmission rate within camps was significantly greater than that between camps, the large pool of susceptible individuals across camps enabled similar numbers of secondary cases generated between versus within camps. CONCLUSIONS: The consideration of clustered transmission and heterogeneous infectiousness is important for understanding NoV transmission dynamics. Models including these mechanisms may be useful for providing early warning and guiding outbreak response.

Children as sentinels of tuberculosis transmission: disease mapping of programmatic data.

BACKGROUND: Identifying hotspots of tuberculosis transmission can inform spatially targeted active case-finding interventions. While national tuberculosis programs maintain notification registers which represent a potential source of data to investigate transmission patterns, high local tuberculosis incidence may not provide a reliable signal for transmission because the population distribution of covariates affecting susceptibility and disease progression may confound the relationship between tuberculosis incidence and transmission. Child cases of tuberculosis and other endemic infectious disease have been observed to provide a signal of their transmission intensity. We assessed whether local overrepresentation of child cases in tuberculosis notification data corresponds to areas where recent transmission events are concentrated. METHODS: We visualized spatial clustering of children < 5 years old notified to Peru's National Tuberculosis Program from two districts of Lima, Peru, from 2005 to 2007 using a log-Gaussian Cox process to model the intensity of the point-referenced child cases. To identify where clustering of child cases was more extreme than expected by chance alone, we mapped all cases from the notification data onto a grid and used a hierarchical Bayesian spatial model to identify grid cells where the proportion of cases among children < 5 years old is greater than expected. Modeling the proportion of child cases allowed us to use the spatial distribution of adult cases to control for unobserved factors that may explain the spatial variability in the distribution of child cases. We compare where young children are overrepresented in case notification data to areas identified as transmission hotspots using molecular epidemiological methods during a prospective study of tuberculosis transmission conducted from 2009 to 2012 in the same setting. RESULTS: Areas in which childhood tuberculosis cases are overrepresented align with areas of spatial concentration of transmission revealed by molecular epidemiologic methods. CONCLUSIONS: Age-disaggregated notification data can be used to identify hotspots of tuberculosis transmission and suggest local force of infection, providing an easily accessible source of data to target active case-finding intervention.

Transmission Modeling with Regression Adjustment for Analyzing Household-based Studies of Infectious Disease: Application to Tuberculosis.

BACKGROUND: Household contacts of people infected with a transmissible disease may be at risk due to this proximate exposure, or from other unobserved sources. Understanding variation in infection risk is essential for targeting interventions. METHODS: We develop an analytical approach to estimate household and exogenous forces of infection, while accounting for individual-level characteristics that affect susceptibility to disease and transmissibility. We apply this approach to a cohort study conducted in Lima, Peru, of 18,544 subjects in 4,500 households with at least one active tuberculosis (TB) case and compare the results to those obtained by Poisson and logistic regression. RESULTS: HIV-coinfected (susceptibility hazard ratio [SHR] = 3.80, 1.56-9.29), child (SHR = 1.72, 1.32-2.23), and teenage (SHR = 2.00, 1.49-2.68) household contacts of TB cases experience a higher hazard of TB than do adult contacts. Isoniazid preventive therapy (SHR = 0.30, 0.21-0.42) and Bacillus Calmette-Guérin (BCG) vaccination (SHR = 0.66, 0.51-0.86) reduce the risk of disease among household contacts. TB cases without microbiological confirmation exert a smaller hazard of TB among their close contacts compared with smear- or culture-positive cases (excess hazard ratio = 0.88, 0.82-0.93 for HIV- cases and 0.82, 0.57-0.94 for HIV+ cases). The extra household force of infection results in 0.01 (95% confidence interval [CI] = 0.004, 0.028) TB cases per susceptible household contact per year and the rate of transmission between a microbiologically confirmed TB case and susceptible household contact at 0.08 (95% CI = 0.045, 0.129) TB cases per pair per year. CONCLUSIONS: Accounting for exposure to infected household contacts permits estimation of risk factors for disease susceptibility and transmissibility and comparison of within-household and exogenous forces of infection.

Effects of Sequential Influenza A(H1N1)pdm09 Vaccination on Antibody Waning.

BACKGROUND: Antibody waning following influenza vaccination has been repeatedly evaluated, but waning has rarely been studied in the context of longitudinal vaccination history. METHODS: We developed a Bayesian hierarchical model to assess the effects of sequential influenza A(H1N1)pdm09 vaccination on hemagglutination inhibition antibody boosting and waning in a longitudinal cohort of older children and adults from 2011 to 2016, a period during which the A(H1N1)pdm09 vaccine strain did not change. RESULTS: Antibody measurements from 2057 serum specimens longitudinally collected from 388 individuals were included. Average postvaccination antibody titers were similar across successive vaccinations, but the rate of antibody waning increased with each vaccination. The antibody half-life was estimated to decrease from 32 months (95% credible interval [CrI], 22-61 months) following first vaccination to 9 months (95% CrI, 7-15 months) following a seventh vaccination. CONCLUSIONS: Although the rate of antibody waning increased with successive vaccination, the estimated antibody half-life was longer than a typical influenza season even among the most highly vaccinated. This supports current recommendations for vaccination at the earliest opportunity. Patterns of boosting and waning might be different with the influenza A(H3N2) subtype, which evolves more rapidly and has been most associated with reduced effectiveness following repeat vaccination.

Social patterning of acute respiratory illnesses in the Household Influenza Vaccine Evaluation (HIVE) Study 2014-2015.

Social patterning of infectious diseases is increasingly recognised. Previous studies of social determinants of acute respiratory illness (ARI) have found that highly educated and lower income families experience more illnesses. Subjective social status (SSS) has also been linked to symptomatic ARI, but the association may be confounded by household composition. We examined SSS and ARI in the Household Influenza Vaccine Evaluation (HIVE) Study in 2014-2015. We used SSS as a marker of social disadvantage and created a workplace disadvantage score for working adults. We examined the association between these measures and ARI incidence using mixed-effects Poisson regression models with random intercepts to account for household clustering. In univariate analyses, mean ARI was higher among children <5 years old (P < 0.001), and females (P = 0.004) at the individual level. At the household level, mean ARI was higher for households with at least one child <5 years than for those without (P = 0.002). In adjusted models, individuals in the lowest tertile of SSS had borderline significantly higher rates of ARI than those in the highest tertile (incidence rate ratio (IRR) 1.34, 95% confidence interval (CI) 0.98-1.92). Households in the lowest tertile of SSS had significantly higher ARI incidence in household-level models (IRR 1.46, 95% CI 1.05-2.03). We observed no association between workplace disadvantage and ARI. We detected an increase in the incidence of ARI for households with low SSS compared with those with high SSS, suggesting that socio-economic position has a meaningful impact on ARI incidence.

A mechanistic spatio-temporal framework for modelling individual-to-individual transmission-With an application to the 2014-2015 West Africa Ebola outbreak.

In recent years there has been growing availability of individual-level spatio-temporal disease data, particularly due to the use of modern communicating devices with GPS tracking functionality. These detailed data have been proven useful for inferring disease transmission to a more refined level than previously. However, there remains a lack of statistically sound frameworks to model the underlying transmission dynamic in a mechanistic manner. Such a development is particularly crucial for enabling a general epidemic predictive framework at the individual level. In this paper we propose a new statistical framework for mechanistically modelling individual-to-individual disease transmission in a landscape with heterogeneous population density. Our methodology is first tested using simulated datasets, validating our inferential machinery. The methodology is subsequently applied to data that describes a regional Ebola outbreak in Western Africa (2014-2015). Our results show that the methods are able to obtain estimates of key epidemiological parameters that are broadly consistent with the literature, while revealing a significantly shorter distance of transmission. More importantly, in contrast to existing approaches, we are able to perform a more general model prediction that takes into account the susceptible population. Finally, our results show that, given reasonable scenarios, the framework can be an effective surrogate for susceptible-explicit individual models which are often computationally challenging.