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During the COVID-19 pandemic, forecasting COVID-19 trends to support planning and response was a priority for scientists and decision makers alike. In the United States, COVID-19 forecasting was coordinated by a large group of universities, companies, and government entities led by the Centers for Disease Control and Prevention and the US COVID-19 Forecast Hub ( https://covid19forecasthub.org ). We evaluated approximately 9.7 million forecasts of weekly state-level COVID-19 cases for predictions 1–4 weeks into the future submitted by 24 teams from August 2020 to December 2021. We assessed coverage of central prediction intervals and weighted interval scores (WIS), adjusting for missing forecasts relative to a baseline forecast, and used a Gaussian generalized estimating equation (GEE) model to evaluate differences in skill across epidemic phases that were defined by the effective reproduction number. Overall, we found high variation in skill across individual models, with ensemble-based forecasts outperforming other approaches. Forecast skill relative to the baseline was generally higher for larger jurisdictions (e.g., states compared to counties). Over time, forecasts generally performed worst in periods of rapid changes in reported cases (either in increasing or decreasing epidemic phases) with 95% prediction interval coverage dropping below 50% during the growth phases of the winter 2020, Delta, and Omicron waves. Ideally, case forecasts could serve as a leading indicator of changes in transmission dynamics. However, while most COVID-19 case forecasts outperformed a naïve baseline model, even the most accurate case forecasts were unreliable in key phases. Further research could improve forecasts of leading indicators, like COVID-19 cases, by leveraging additional real-time data, addressing performance across phases, improving the characterization of forecast confidence, and ensuring that forecasts were coherent across spatial scales. In the meantime, it is critical for forecast users to appreciate current limitations and use a broad set of indicators to inform pandemic-related decision making.

Background Those responding to humanitarian crises have an ethical imperative to respond most where the need is greatest. Metrics are used to estimate the severity of a given crisis. The INFORM Severity Index, one such metric, has become widely used to guide policy makers in humanitarian response decision making. The index, however, has not undergone critical statistical review. If imprecise or incorrect, the quality of decision making for humanitarian response will be affected. This analysis asks, how precise and how well does this index reflect the severity of conditions for people affected by disaster or war? Results The INFORM Severity Index is calculated from 35 publicly available indicators, which conceptually reflect the severity of each crisis. We used 172 unique global crises from the INFORM Severity Index database that occurred January 1 to November 30, 2019 or were ongoing by this date. We applied exploratory factor analysis (EFA) to determine common factors within the dataset. We then applied a second-order confirmatory factor analysis (CFA) to predict crisis severity as a latent construct. Model fit was assessed via chi-square goodness-of-fit statistic, Comparative Fit Index (CFI), Tucker-Lewis Index (TLI), and Root Mean Square Error of Approximation (RMSEA). The EFA models suggested a 3- or 4- factor solution, with 46 and 53% variance explained in each model, respectively. The final CFA was parsimonious, containing three factors comprised of 11 indicators, with reasonable model fit (Chi-squared = 107, with 40 degrees of freedom, CFI = 0.94, TLI = 0.92, RMSEA = 0.10). In the second-order CFA, the magnitude of standardized factor-loading on the ‘societal governance’ latent construct had the strongest association with the latent construct of ‘crisis severity’ (0.73), followed by the ‘humanitarian access/safety’ construct (0.56). Conclusions A metric of crisis-severity is a critical step towards improving humanitarian response, but only when it reflects real life conditions. Our work is a first step in refining an existing framework to better quantify crisis severity.

Understanding the dynamics of antibody responses following vaccination and SARS-CoV-2 infection is important for informing effective vaccination strategies and other public health interventions. This study investigates SARS-CoV-2 antibody dynamics in a Puerto Rican cohort, analyzing how IgG levels vary by vaccination status and previous infection. We assess waning immunity and the distribution of hybrid immunity with the aim to inform public health strategies and vaccination programs in Puerto Rico and similar settings. We conducted a prospective, longitudinal cohort study to identify SARS-CoV-2 infections and related outcomes in Ponce, Puerto Rico, from June 2020–August 2022. Participants provided self-collected nasal swabs every week and serum every six months for RT-PCR and IgG testing, respectively. IgG reactivity against nucleocapsid (N) antigens, which generally indicate previous infection, and spike (S1) and receptor-binding domain (RBD) antigens, which indicate history of either infection or vaccination, was assessed using the Luminex Corporation xMAP® SARS-CoV-2 Multi-Antigen IgG Assay. Prior infection was defined by positive RT-PCRs, categorized by the predominant circulating SARS-CoV-2 variant at the event time. Demographic information, medical history, and COVID-19 vaccination history were collected through standardized questionnaires. Of 882 participants included in our analysis, 34.0% experienced at least one SARS-CoV-2 infection, with most (78.7%) occurring during the Omicron wave (December 2021 onwards). SARS-CoV-2 antibody prevalence increased over time, reaching 98.4% by the final serum collection, 67.0% attributable to vaccination alone, 1.6% from infection alone, and 31.4% from both. Regardless of prior infection status, RBD and S1 IgG levels gradually declined following two vaccine doses. A third dose boosted these antibody levels and showed a slower decline over time. N-antibody levels peaked during the Omicron surge and waned over time. Vaccination in individuals with prior SARS-CoV-2 infection elicited the highest and most durable antibody responses. N or S1 seropositivity was associated with lower odds of a subsequent positive PCR test during the Omicron period, with N antibodies showing a stronger association. By elucidating the differential decay of RBD and S1 antibodies following vaccination and the complexities of N-antibody response following infection, this study in a Puerto Rican cohort strengthens the foundation for developing targeted interventions and public health strategies.

Global risk maps are an important tool for assessing the global threat of mosquito and tick-transmitted arboviral diseases. Public health officials increasingly rely on risk maps to understand the drivers of transmission, forecast spread, identify gaps in surveillance, estimate disease burden, and target and evaluate the impact of interventions. Here, we describe how current approaches to mapping arboviral diseases have become unnecessarily siloed, ignoring the strengths and weaknesses of different data types and methods. This places limits on data and model output comparability, uncertainty estimation and generalisation that limit the answers they can provide to some of the most pressing questions in arbovirus control. We argue for a new generation of risk mapping models that jointly infer risk from multiple data types. We outline how this can be achieved conceptually and show how this new framework creates opportunities to better integrate epidemiological understanding and uncertainty quantification. We advocate for more co-development of risk maps among modellers and end-users to better enable risk maps to inform public health decisions. Prospective validation of risk maps for specific applications can inform further targeted data collection and subsequent model refinement in an iterative manner. If the expanding use of arbovirus risk maps for control is to continue, methods must develop and adapt to changing questions, interventions and data availability.

Background The world’s second largest Ebola outbreak occurred in the Democratic Republic of Congo from 2018 to 2020. At the time, risk of cross-border spread into South Sudan was very high. Thus, the South Sudan Ministry of Health scaled up Ebola preparedness activities in August 2018, including implementation of a 24-h, toll-free Ebola virus disease (EVD) hotline. The primary purpose was the hotline was to receive EVD alerts and the secondary goal was to provide evidence-based EVD messages to the public. Methods To assess whether the hotline augmented Ebola preparedness activities in a protracted humanitarian emergency context, we reviewed 22 weeks of call logs from January to June 2019. Counts and percentages were calculated for all available data. Results The hotline received 2114 calls during the analysis period, and an additional 1835 missed calls were documented. Callers used the hotline throughout 24-h of the day and were most often men and individuals living in Jubek state, where the national capital is located. The leading reasons for calling were to learn more about EVD (68%) or to report clinical signs or symptoms (16%). Common EVD-related questions included EVD signs and symptoms, transmission, and prevention. Only one call was documented as an EVD alert, and there was no documentation of reported symptoms or whether the person met the EVD case definition. Conclusions Basic surveillance information was not collected from callers. To trigger effective outbreak investigation from hotline calls, the hotline should capture who is reporting and from where, symptoms and travel history, and whether this information should be further investigated. Electronic data capture will enhance data quality and availability of information for review. Additionally, the magnitude of missed calls presents a major challenge. When calls are answered, there is potential to provide health communication, so risk communication needs should be considered. However, prior to hotline implementation, governments should critically assess whether their hotline would yield actionable data and if other data sources for surveillance or community concerns are available.

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks.

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages.

During the COVID-19 pandemic, forecasting COVID-19 trends to support planning and response was a priority for scientists and decision makers alike. In the United States, COVID-19 forecasting was coordinated by a large group of universities, companies, and government entities led by the Centers for Disease Control and Prevention and the US COVID-19 Forecast Hub (https://covid19forecasthub.org). We evaluated approximately 9.7 million forecasts of weekly state-level COVID-19 cases for predictions 1–4 weeks into the future submitted by 24 teams from August 2020 to December 2021. We assessed coverage of central prediction intervals and weighted interval scores (WIS), adjusting for missing forecasts relative to a baseline forecast, and used a Gaussian generalized estimating equation (GEE) model to evaluate differences in skill across epidemic phases that were defined by the effective reproduction number. Overall, we found high variation in skill across individual models, with ensemble-based forecasts outperforming other approaches. Forecast skill relative to the baseline was generally higher for larger jurisdictions (e.g., states compared to counties). Over time, forecasts generally performed worst in periods of rapid changes in reported cases (either in increasing or decreasing epidemic phases) with 95% prediction interval coverage dropping below 50% during the growth phases of the winter 2020, Delta, and Omicron waves. Ideally, case forecasts could serve as a leading indicator of changes in transmission dynamics. However, while most COVID-19 case forecasts outperformed a naïve baseline model, even the most accurate case forecasts were unreliable in key phases. Further research could improve forecasts of leading indicators, like COVID-19 cases, by leveraging additional real-time data, addressing performance across phases, improving the characterization of forecast confidence, and ensuring that forecasts were coherent across spatial scales. In the meantime, it is critical for forecast users to appreciate current limitations and use a broad set of indicators to inform pandemic-related decision making.

Access to sanitation reduces pathogen exposure to improve child health by reducing diarrhea and promoting physical growth. Globally, millions of children under five suffer from diarrheal disease and undernutrition, which are leading causes of child-mortality in low-resource settings and have long term consequences for children that do survive. Recent sanitation interventions, however, have shown no effect on improving child health outcomes. The null results of an established intervention, therefore, require further investigation into the mechanisms linking sanitation access to improved health outcomes. This dissertation seeks to highlight the role of human behavior as it relates to latrine access, as well as address how sanitation and nutrition intervention affect environmental and biological processes driving enteropathogen transmission. In chapter two, we use health behavior theory to identify determinants of latrine use behavior from a sanitation-related ethnography collected in rural, Ecuadorian communities. We then develop a quantitative survey tool to collect information on these determinants, and following primary data collection, apply data reduction approaches and regression analyses to select which determinants in the sample are reflective of self-reported consistent latrine use. We show that latrine use is influenced by a constellation of drivers, including social norms, attitudes about latrine cleanliness, and habitual latrine use behavior. In chapter three, which also uses primary data from rural Ecuador, we use a suite of determinants to predict an individual’s propensity to consistently use a latrine via latent class analysis modeling. We find that latrine use behavior is most accurately predicted by community-level norms reflecting other people’s latrine use and attitudes towards latrine sharing. We also illustrate that latrine access is not the primary driver of latrine use. In chapter four, we build a community-level environmental-mediated enteropathogen transmission model that reflects the biological interdependencies between enteric infection and undernutrition. By simulating enteric pathogen transmission among a cohort of children, we test the effect of sanitation and nutritional interventions on the overall transmission of disease. The mathematical modeling approach allows for exploration of underlying mechanism inherent in this system, which highlights opportunities to inform intervention design. Overall, in each chapter, we use a variety of methods to examine environmental, community, and individual-level processes at play in a system of latrine access, latrine use, and diarrhea-related morbidity. Towards the goal of implementing effective sanitation interventions, this dissertation argues for a need to improve measurement of sanitation behavior and incorporate enteric pathogen transmission dynamics in programmatic design, implementation, and evaluation.

Metals are suspected contributors of autoimmune disease among indigenous Americans. However, the association between metals exposure and biomarkers of autoimmunity is under-studied. In Nicaragua, environmental exposure to metals is also largely unexamined with regard to autoimmunity. We analyzed pooled and stratified exposure and outcome data from Navajo (n = 68) and Nicaraguan (n = 47) men of similar age and health status in order to characterize urinary concentrations of metals, compare concentrations with the US National Health and Nutrition Examination Survey (NHANES) male population, and examine the associations with biomarkers of autoimmunity. Urine samples were analyzed for metals via inductively coupled plasma mass spectrometry (ICP-MS) at the US Centers for Disease Control and Prevention. Serum samples were examined for antinuclear antibodies (ANA) at 1:160 and 1:40 dilutions, using an indirect immunofluorescence assay and for specific autoantibodies using enzyme-linked immunosorbent assay (ELISA). Logistic regression analyses evaluated associations of urinary metals with autoimmune biomarkers, adjusted for group (Navajo or Nicaraguan), age, and seafood consumption. The Nicaraguan men had higher urinary metal concentrations compared with both NHANES and the Navajo for most metals; however, tin was highest among the Navajo, and uranium was much higher in both populations compared with NHANES. Upper tertile associations with ANA positivity at the 1:160 dilution were observed for barium, cesium, lead, strontium and tungsten.