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BackgroundUptake of COVID-19 bivalent vaccines and oral medication nirmatrelvir-ritonavir (Paxlovid) has remained low across the United States. Assessing the public health impact of increasing uptake of these interventions in key risk groups can guide further public health resources and policy.MethodsThis modeling study used person-level data from the California Department of Public Health on COVID-19 cases, hospitalizations, deaths, and vaccine administration from July 23, 2022 to January 23, 2023. We modeled the impact of additional uptake of bivalent COVID-19 vaccines and nirmatrelvir-ritonavir during acute illness in different risk groups defined by age (50+, 65+, 75+ years) and vaccination status (everyone, primary series only, previously vaccinated). We predicted the number of averted COVID-19 cases, hospitalizations, and deaths and number needed to treat (NNT).ResultsFor both bivalent vaccines and nirmatrelvir-ritonavir, the most efficient strategy (based on NNT) for averting severe COVID-19 was targeting the 75+ years group. We predicted that perfect coverage of bivalent boosters in the 75+ years group would avert 3,920 hospitalizations (95%UI: 2,491-4,882; 7.8% total averted; NNT 387) and 1,074 deaths (95%UI: 774-1,355; 16.2% total averted; NNT 1,410). Perfect uptake of nirmatrelvir-ritonavir in the 75+ years group would avert 5,644 hospitalizations (95%UI: 3,947-6,826; 11.2% total averted; NNT 11) and 1,669 deaths (95%UI: 1,053-2,038; 25.2% total averted; NNT 35).ConclusionsThese findings suggest prioritizing uptake of bivalent boosters and nirmatrelvir-ritonavir among the oldest age groups would be efficient and have substantial public health impact in reducing the burden of severe COVID-19, but would not address the entire burden of severe COVID-19.

By November 30, 2021, approximately 130,781 COVID-19-associated deaths, one in six of all U.S. deaths from COVID-19, had occurred in California and New York.* COVID-19 vaccination protects against infection with SARS-CoV-2 (the virus that causes COVID-19), associated severe illness, and death (1,2); among those who survive, previous SARS-CoV-2 infection also confers protection against severe outcomes in the event of reinfection (3,4). The relative magnitude and duration of infection- and vaccine-derived protection, alone and together, can guide public health planning and epidemic forecasting. To examine the impact of primary COVID-19 vaccination and previous SARS-CoV-2 infection on COVID-19 incidence and hospitalization rates, statewide testing, surveillance, and COVID-19 immunization data from California and New York (which account for 18% of the U.S. population) were analyzed. Four cohorts of adults aged ≥18 years were considered: persons who were 1) unvaccinated with no previous laboratory-confirmed COVID-19 diagnosis, 2) vaccinated (14 days after completion of a primary COVID-19 vaccination series) with no previous COVID-19 diagnosis, 3) unvaccinated with a previous COVID-19 diagnosis, and 4) vaccinated with a previous COVID-19 diagnosis. Age-adjusted hazard rates of incident laboratory-confirmed COVID-19 cases in both states were compared among cohorts, and in California, hospitalizations during May 30-November 20, 2021, were also compared. During the study period, COVID-19 incidence in both states was highest among unvaccinated persons without a previous COVID-19 diagnosis compared with that among the other three groups. During the week beginning May 30, 2021, compared with COVID-19 case rates among unvaccinated persons without a previous COVID-19 diagnosis, COVID-19 case rates were 19.9-fold (California) and 18.4-fold (New York) lower among vaccinated persons without a previous diagnosis; 7.2-fold (California) and 9.9-fold lower (New York) among unvaccinated persons with a previous COVID-19 diagnosis; and 9.6-fold (California) and 8.5-fold lower (New York) among vaccinated persons with a previous COVID-19 diagnosis. During the same period, compared with hospitalization rates among unvaccinated persons without a previous COVID-19 diagnosis, hospitalization rates in California followed a similar pattern. These relationships changed after the SARS-CoV-2 Delta variant became predominant (i.e., accounted for50% of sequenced isolates) in late June and July. By the week beginning October 3, compared with COVID-19 cases rates among unvaccinated persons without a previous COVID-19 diagnosis, case rates among vaccinated persons without a previous COVID-19 diagnosis were 6.2-fold (California) and 4.5-fold (New York) lower; rates were substantially lower among both groups with previous COVID-19 diagnoses, including 29.0-fold (California) and 14.7-fold lower (New York) among unvaccinated persons with a previous diagnosis, and 32.5-fold (California) and 19.8-fold lower (New York) among vaccinated persons with a previous diagnosis of COVID-19. During the same period, compared with hospitalization rates among unvaccinated persons without a previous COVID-19 diagnosis, hospitalization rates in California followed a similar pattern. These results demonstrate that vaccination protects against COVID-19 and related hospitalization, and that surviving a previous infection protects against a reinfection and related hospitalization. Importantly, infection-derived protection was higher after the Delta variant became predominant, a time when vaccine-induced immunity for many persons declined because of immune evasion and immunologic waning (2,5,6). Similar cohort data accounting for booster doses needs to be assessed, as new variants, including Omicron, circulate. Although the epidemiology of COVID-19 might change with the emergence of new variants, vaccination remains the safest strategy to prevent SARS-CoV-2 infections and associated complications; all eligible persons should be up to date with COVID-19 vaccination. Additional recommendations for vaccine doses might be warranted in the future as the virus and immunity levels change.

A rare example of a successful long-term elimination of the mosquito Aedes aegypti is in Brisbane, Queensland, where the legislatively-enforced removal of rainwater tanks drove its disappearance by the mid-1950s. However, a decade-long drought led to the mass installation of rainwater tanks throughout the region, re-introducing critical breeding sites for the mosquito’s persistence in this subtropical region. With Ae. aegypti re-invading towns just 150 km north of Brisbane, we examined the potential for their sustained elimination. Through genomic analyses, we estimated historical expansion and current isolation between neighboring populations as close as 15 kilometers. The estimated recent migration rate, entomological and meteorological data were used to calibrate the simulations of elimination campaigns in the two southernmost populations. Our simulations indicate that Ae. aegypti could be eliminated with moderate release numbers of incompatible Wolbachia-infected (IIT) males (sorted with an error rate ≤10-6) if non-compliant rainwater tanks are removed first. With this combined campaign, highly effective suppression (>99%) was predicted in both towns, and complete elimination was predicted in 35% of simulations in one town. Without tank removal, however, IIT led to a moderate suppression (61-93%) even with a 40:1 ratio of released IIT males to local males. Moreover, with a ratio of >20:1, Wolbachia establishment was predicted when the sorting error was >10-7. Our conservative estimates of intervention outcomes inform the planning of Ae. aegypti elimination in the region, and offer insight into the effective combinations of conventional and novel control tools, particularly for vulnerable mosquito populations at range margins.SignificanceAfter decades of range stagnation in Australia, the Aedes aegypti mosquito is expanding southward, approaching the most-densely-populated areas of Queensland. Using population genomics and simulation modeling of elimination campaigns, we show that Australia’s southernmost populations of this disease vector are genetically isolated and could be eliminated with moderate releases of incompatible Wolbachia-infected males if major larval breeding sites (non-compliant rainwater tanks) are removed first. The risk of Wolbachia establishment for this approach is low, and so is the risk of quick mosquito re-invasion. Our conservative estimates of intervention outcomes inform the planning of Ae. aegypti elimination in the region, and offer new insight into the benefits of combining conventional and novel control tools, particularly for mosquito populations at range margins.

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.

In analysis of survival outcomes supplemented with both clinical information and high-dimensional gene expression data, use of the traditional Cox proportional hazards model fails to meet some emerging needs in biomedical research. First, the number of covariates is generally much larger the sample size. Secondly, predicting an outcome based on individual gene expression is inadequate because multiple biological processes and functional pathways regulate phenotypic expression. Another challenge is that the Cox model assumes that populations are homogenous, implying that all individuals have the same risk of death, which is rarely true due to unmeasured risk factors among populations. In this paper we propose group LASSO with gamma-distributed frailty for variable selection in Cox regression by extending previous scholarship to account for heterogeneity among group structures related to exposure and susceptibility. The consistency property of the proposed method is established. This method is appropriate for addressing a wide variety of research questions from genetics to air pollution. Simulated and real world data analysis shows promising performance by group LASSO compared with other methods, including group SCAD and group MCP. Future research directions include expanding the use of frailty with adaptive group LASSO and sparse group LASSO methods.

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.

Background Vector-borne diseases are a major cause of disease burden in Guayaquil, Ecuador, especially arboviruses spread by Aedes aegypti mosquitoes. Understanding which household characteristics and risk factors lead to higher Ae. aegypti densities and consequent disease risk can help inform and optimize vector control programs. Methods Cross-sectional entomological surveys were conducted in Guayaquil between 2013 and 2016, covering household demographics, municipal services, potential breeding containers, presence of Ae. aegypti larvae and pupae, and history of using mosquito control methods. A zero-truncated negative binomial regression model was fitted to data for estimating the household pupal index. An additional model assessed the factors of the most productive breeding sites across all of the households. Results Of surveyed households, 610 satisfied inclusion criteria. The final household-level model found that collection of large solid items (e.g., furniture and tires) and rainfall the week of and 2 weeks before collection were negatively correlated with average pupae per container, while bed canopy use, unemployment, container water volume, and the interaction between large solid collection and rainfall 2 weeks before the sampling event were positively correlated. Selection of these variables across other top candidate models with ∆AICc Ae. aegypti pupae counts while breeding sites composed of car parts, furniture, sewerage parts, vases, were all negatively correlated. Conclusions Having access to municipal services like bulky item pickup was effective at reducing mosquito proliferation in households. Association of bed canopy use with higher mosquito densities is unexpected, and may be a consequence of large local mosquito populations or due to limited use or effectiveness of other vector control methods. The impact of rainfall on mosquito density is multifaceted, as it may both create new habitat and “wash out” existing habitat. Providing services and social/technical interventions focused on monitoring and eliminating productive breeding sites is important for reducing aquatic-stage mosquito densities in households at risk for Ae. aegypti-transmitted diseases. Graphical Abstract

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.

Human infection with the Southeast Asian liver fluke Opisthorchis viverrini and liver fluke-associated cholangiocarcinoma cause significant disease burden in Southeast Asia. While there has been considerable work to understand liver fluke pathology and to reduce infection prevalence, there remains a limited understanding of the environmental determinants of parasite transmission dynamics to inform treatment and control programs. A particular setting where targeted control efforts have taken place is the Lawa Lake complex in northeast Thailand. Here, we describe the recent history of host infections, as well as the hydrologic characteristics of this floodplain ecosystem that influence the extent of snail habitat and fish mobility and the transport of human waste and parasite cercariae. Using mathematical modeling, we outline a framework for reconstructing environmental transmission of O. viverrini over the course of the Lawa Project control program from its inception in 2008 until 2016, using locally acquired but fragmentary longitudinal infection data for both humans and environmental hosts. The role of water flow in facilitating movement between snail, fish, human, and reservoir hosts is a particular focus with respect to its relevant scales and its impact on success of interventions. In this setting, we argue that an understanding of the key environmental drivers of disease transmission processes is central to the effectiveness of any environmental intervention.

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.

Land use regression (LUR) models have proven to be a robust technique for predicting spatial distribution of pollutants with high resolution. Wind direction is an important factor affecting atmospheric environment quality. However, conventional LUR models have difficulties taking wind direction into consideration. This study put forward a semicircular-buffer-based (SCBB) LUR model to overcome this challenge. To assess the impact of wind direction on model performance, we set up two different LUR models for nitrogen dioxide (NO2) and particulate matter (PM10) in the urban area of Changsha, China. A location-allocation approach was used to identify sampling sites. Integrated 14-day mean concentrations of NO2 and PM10 were measured at 80 sites and 40 sites, respectively. Measured mean concentrations ranged from 17.0 to 75.7 for NO2 and 34.7 to 118.7 μg/m3 for PM10. Random samples of 75% of monitoring sites were used to the develop model and the remaining 25% of sites were retained for evaluation. Predictor variables were created in a geographic information system (GIS) and LUR models were developed with the most significant variables. The results showed SCBB LUR models had significantly higher R2 values than traditional LUR models, supporting the feasibility of this new approach incorporating wind direction in the LUR model.

Particulate matter (PM) pollution in China has an obvious characteristic of spatial distribution. It is well known that intensive anthropogenic activities, such as fossil fuel combustion and biomass burning, have great influence on the spatial distribution of PM pollution. However, the spacescale-dependent relationships between PM concentrations and weather conditions remain unclear. Here, we investigated the characteristics of two types of particulate pollution, including PM2.5 and PM10, and their spatial relationships with meteorological elements in 173 cities throughout China from March 2014 to February 2015. Results: (1) High PM2.5 concentrations were distinctly located southeast of the Hu Line, and high PM10 concentrations were distinctly situated north of the Yangtze River; (2) Spacescale-dependent relationships were found between PM pollution and meteorological elements. The influence of temperature had similar inverted V-shaped characteristics, namely, there was serious PM pollution when temperature was about 15 °C, and there was slight PM pollution when temperature was less or more than 15 °C. Annual precipitation, wind speed, and relative humidity were negatively correlated with PM, while annual atmospheric pressure was positively correlated with PM; (3) The ideal meteorological regions were identified according to the quantified spatial relationships between PM and meteorological elements, which could be defined by a combination of the following conditions: (a) temperature 21 °C; (b) precipitation >1500 mm; (c) atmospheric pressure 3 m/s; and (e) relative humidity >65%, where air pollutants can easily be scavenged. The success of this research provides a meteorological explanation to the spatial distribution characteristics of PM pollution in China.

It is pressing to investigate the influence factors of eutrophication in Dongting Lake, which is the second largest freshwater lake in China and is faced with more and more serious eutrophication problems by natural and man-made factors in recent years. In this study, a new multivariate analysis approach integrating the maximum likelihood estimation structural equation modelling (ML-SEM) with neural network method is proposed. Twenty-seven variables indicating water quality from 2005 to 2012 were derived from the Dongting Lake Ecological Environment Monitoring Center of Hunan Province. The results showed that: (1) temperature (T) and hydraulic gradient (HG) had the most positive influence on chlorophyll-a (Chl-a) with a sensitivity ratio of 1.218 and 1.039, respectively, (2) transparency (SD) had the most negative effect on Chl-a concentration and (3) Chl-a increased quickly and reached the highest level when total phosphorus (TP) levels were from 0.08 mg/L to 0.12 mg/L. These results would provide guidance for eutrophication control strategies for Dongting Lake. By combining ML-SEM with artificial intelligence technique such as neural network, this study examined the causal and nonlinear relationships involved in the eutrophication process.