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Vaccination and nonpharmaceutical interventions (NPIs) reduce transmission of severe acute respiratory syndrome coronavirus 2 infection, but their effectiveness depends on coverage and adherence levels. We used scenario modeling to evaluate their effects on cases and deaths averted and herd immunity. NPIs and vaccines worked synergistically in different parts of the pandemic to reduce disease burden.

Background: For countries that have only recently started COVID-19 vaccinations, there remains a key public health question of who should be prioritized for early vaccination. Most vaccine prioritization analyses have only considered variation in risk of infection and death by age. We provide a more granular analysis with stratification by demographics, risk factors, and location. Methods: We used a simulation model to compare the impact of different prioritization strategies on COVID-19 cases, deaths and disability-adjusted life years (DALYs) over the first 6 months of vaccine rollout. We calibrated the model to demographic and location data on 28,175 COVID-19 deaths in California up to December 30, 2020, and incorporated variation in risk by occupation and comorbidity status using published estimates. We estimated the proportion of clinical cases, deaths and DALYs averted relative to a scenario of no vaccination for strategies prioritizing vaccination by a single risk factor (special population status (e.g. incarcerated individual), age, essential worker status, comorbidity status) or multiple risk factors (e.g. age and location). Results: We found that age-based targeting averted the most deaths (65% for 5 million individuals vaccinated) and DALYs (40%) of strategies targeting by a single risk factor and targeting essential workers averted the least deaths (33%) and DALYs (25%) over the first 6 months of vaccine rollout. However, targeting by two or more risk factors simultaneously averted up to 40% more DALYs. Conclusions: Our findings highlight the potential value of multiple-risk-factor targeting of COVID-19 vaccination. Where vaccine supply is limited and logistical challenges in vaccine delivery persist, age-based targeting offers a means of ensuring that vaccines reach those most at risk of poor health outcomes. If operational challenges can be overcome, more granular vaccination strategies that overlap age with other risk factors can be adopted.

Vaccination and non-pharmaceutical interventions (NPIs) reduce transmission of SARS-CoV-2 infection, but their effectiveness depends on coverage and adherence levels. We used scenario modeling to evaluate their effects on cases and deaths averted and herd immunity. NPIs and vaccines worked synergistically in different parts of the pandemic to reduce disease burden.

A critical question in the COVID-19 pandemic is how to optimally allocate the first available vaccinations to maximize health impact. We used a static simulation model with detailed demographic and risk factor stratification to compare the impact of different vaccine prioritization strategies in the United States on key health outcomes, using California as a case example. We calibrated the model to demographic and location data on 28,175 COVID-19 deaths in California up to December 30, 2020, and incorporated variation in risk by occupation and comorbidity status using published estimates. We predicted the proportion of COVID-19 clinical cases, deaths and disability-adjusted life years (DALYs) averted over 6 months relative to a scenario of no vaccination for five vaccination strategies that prioritized vaccination by a single risk factor: random allocation; targeting special populations (e.g. incarcerated individuals); targeting older individuals; targeting essential workers; and targeting individuals with comorbidities. Targeting older individuals averted the highest proportion of DALYs (40% for 5 million individuals vaccinated) and deaths (65%) but the lowest proportion of cases (12%). Targeting essential workers averted the lowest proportion of DALYs (25%) and deaths (33%). Allocating vaccinations simultaneously by age and location or by age, sex, race/ethnicity, location, occupation, and comorbidity status averted a significantly higher proportion of DALYs (48% and 56%) than any strategy prioritizing by a single risk factor. Our results corroborate findings of other studies that age targeting is the best single-risk-factor prioritization strategy for averting DALYs, and suggest that targeting by multiple risk factors would provide additional benefit. Significance statement COVID-19 has caused a global pandemic, and a key public health question is who should get the first available vaccinations. Most vaccine prioritization analyses have only considered variation in risk of infection and death by age and occupation. We provide a more granular analysis with stratification by demographics, risk factors, and location. We predict the impact of different prioritization strategies on COVID-19 cases, deaths and disability-adjusted life years (DALYs). We find that age-based targeting averts the most deaths and DALYs of strategies targeting by a single risk factor, but that targeting by two or more risk factors simultaneously would avert significantly more deaths and DALYs. Our findings highlight the potential value of multiple-risk-factor targeting of vaccination when supply is limited.

Transmissibility is a significant factor in parasite fitness. The rate and magnitude of parasite transmission affect prevalence and infection intensity in individual hosts and are influenced by environmental factors. In this context, the objectives of this study were: (i) to experimentally assess Opisthorchis viverrini miracidia survival and infectivity over time and across temperatures; and (ii) to combine these experimental results with environmental data to build a key component of a transmission model, identifying seasonal windows of transmission risk in hyper-endemic northeastern Thailand. Five replicates of 50 O. viverrini eggs were randomly distributed and maintained under four temperature conditions (25°C, 30°C, 35°C, 40°C). Microscopic observations were performed on all experimental units over a period of 3 months to record miracidia motility and mortality trends. Six infection trials were also conducted to assess infectivity of miracidia over time and across temperatures, using observations of egg hatching success and infection rates. Upon completion of experiments, data were integrated into a transmission model to create a Transmission Risk Index and to simulate seasonal transmission risk. Miracidia survival rate and motility decreased steadily with 50% mortality observed after 2 weeks. Hatching and infection success also decreased significantly after 3 weeks. Temperatures over 30°C were associated with increased mortality and decreased infectivity. When incorporating local environmental parameters into our model, we observed low transmission risk during the dry season and increasing transmission risk at the onset of the rainy season, culminating with the highest risk in September. We believe that our results provide the first estimates of O.viverrini miracidia survival and transmission potential under variable temperature conditions and suggest that high temperature treatment (> 40°C) of fecal waste could be an efficient control strategy.

A metapopulation model linking local hydrology with transmission of the liver fluke Opisthorchis viverrini in a lake system in northeast Thailand was developed and parameterized using infection data from 2008-2016. A rainfall-runoff model and other hydrologic data were used to assess level of connectivity between villages and the influence of upstream communities on parasite distribution in the study area, while the disease transmission model was expanded from a single-village model for O. viverrini transmission. Connectivity between villages and hydrologic variables was assessed monthly and showed strong seasonality trends. The metapopulation model improved upon the single-village model in its fit to historical data patterns for the six village clusters with the introduction of the new time-variable parameters. Results suggest there are three unique hydrologic-epidemiologic regimes within the Lawa Lake system in response to upstream watersheds and risk of overland flooding that contribute to risk for O. viverrini infection. Similar approaches using a hydrologic submodel to inform a mechanistic disease transmission model could be applied across many water-related disease systems. Author Summary While hydrology is intuitively understood to influence transmission dynamics of water-related diseases, limited research exists that explicitly links hydrologic and infectious disease data. In this work, we use an approach that leverages a rainfall-runoff model to better understand water movement into, out of, and around Lawa Lake in northeast Thailand and how that affects fate and transport of the multiple waterborne life stages of Opisthorchis viverrini . To model disease transmission, we represent six village clusters around the lake using known infection prevalence data of humans, cats and dogs, snails, and fish to parameterize and fit a metapopulation model. The connectivity between village clusters and external inputs of parasites are derived from the hydrology data and the rainfall-runoff model. Results suggest three unique hydrologic regimes that also reflect unique patterns in disease prevalence among the different hosts. Other water-related disease systems can use similar approaches to assess the impacts of water on pathogen transmission dynamics.

Land use regression (LUR) models are widely used in epidemiological studies to assess exposure to air pollution. However, most of the existing LUR studies focus on estimating annual or monthly average concentration of air pollutants, with high spatial but low temporal resolution. In this paper, we combined LUR models with meteorological conditions to estimate daily nitrogen dioxide (NO2) and particulate matter (PM10) concentrations in the urban area of Changsha, China. Seventy-four sites for NO2 and thirty-six sites for PM10 were selected to build LUR models. The LUR models explained 51% and 62% of spatial variability for NO2 and PM10. The most important spatial explanatory variables included major roads, residential land and public facilities land, indicating that the spatial distributions of NO2 and PM10 are closely related to traffic conditions and human activities. Meteorological factors were introduced to model the temporal variability of NO2 and PM10 by using meteorological factors regression (MFR) and back propagation neural network (BPNN) modeling techniques. Important temporal explanatory variables included temperature, wind speed, cloud cover and percentage of haze. Pearson's r values between predicted and measured concentrations were much higher in BPNN models than in MFR models. The results demonstrate that the BPNN models showed a better performance than the MFR models in modeling temporal variation of NO2 and PM10. The approach of modeling spatial and temporal variation of air pollutants using LUR models coupled with meteorological conditions has potential usefulness for exposure assessment, especially for medium or short term exposure, in health studies.