Repeatability and timing of tropical influenza epidemics.

Much of the world experiences influenza in yearly recurring seasons, particularly in temperate areas. These patterns can be considered repeatable if they occur predictably and consistently at the same time of year. In tropical areas, including southeast Asia, timing of influenza epidemics is less consistent, leading to a lack of consensus regarding whether influenza is repeatable. This study aimed to assess repeatability of influenza in Vietnam, with repeatability defined as seasonality that occurs at a consistent time of year with low variation. We developed a mathematical model incorporating parameters to represent periods of increased transmission and then fitted the model to data collected from sentinel hospitals throughout Vietnam as well as four temperate locations. We fitted the model for individual (sub)types of influenza as well as all combined influenza throughout northern, central, and southern Vietnam. Repeatability was evaluated through the variance of the timings of peak transmission. Model fits from Vietnam show high variance (sd = 64-179 days) in peak transmission timing, with peaks occurring at irregular intervals and throughout different times of year. Fits from temperate locations showed regular, annual epidemics in winter months, with low variance in peak timings (sd = 32-57 days). This suggests that influenza patterns are not repeatable or seasonal in Vietnam. Influenza prevention in Vietnam therefore cannot rely on anticipation of regularly occurring outbreaks.

Benefits of near-universal vaccination and treatment access to manage COVID-19 burden in the United States.

BACKGROUND: As we continue the fourth year of the COVID-19 epidemic, SARS-CoV-2 infections still cause high morbidity and mortality in the United States. During 2020-2022, COVID-19 was one of the leading causes of death in the United States and by far the leading cause among infectious diseases. Vaccination uptake remains low despite this being an effective burden reducing intervention. The development of COVID-19 therapeutics provides hope for mitigating severe clinical outcomes. This modeling study examines combined strategies of vaccination and treatment to reduce the burden of COVID-19 epidemics over the next decade. METHODS: We use a validated mathematical model to evaluate the reduction of incident cases, hospitalized cases, and deaths in the United States through 2033 under various levels of vaccination and treatment coverage. We assume that future seasonal transmission patterns for COVID-19 will be similar to those of influenza virus and account for the waning of infection-induced immunity and vaccine-induced immunity in a future with stable COVID-19 dynamics. Due to uncertainty in the duration of immunity following vaccination or infection, we consider three exponentially distributed waning rates, with means of 365 days (1 year), 548 days (1.5 years), and 730 days (2 years). We also consider treatment failure, including rebound frequency, as a possible treatment outcome. RESULTS: As expected, universal vaccination is projected to eliminate transmission and mortality. Under current treatment coverage (13.7%) and vaccination coverage (49%), averages of 81,000-164,600 annual reported deaths, depending on duration of immunity, are expected by the end of this decade. Annual mortality in the United States can be reduced below 50,000 per year with 52-80% annual vaccination coverage and below 10,000 annual deaths with 59-83% annual vaccination coverage, depending on duration of immunity. Universal treatment reduces hospitalizations by 88.6% and deaths by 93.1% under current vaccination coverage. A reduction in vaccination coverage requires a comparatively larger increase in treatment coverage in order for hospitalization and mortality levels to remain unchanged. CONCLUSIONS: Adopting universal vaccination and universal treatment goals in the United States will likely lead to a COVID-19 mortality burden below 50,000 deaths per year, a burden comparable to that of influenza virus.

Estimating case fatality risk of severe Yellow Fever cases: systematic literature review and meta-analysis.

BACKGROUND: Case fatality risk (CFR), commonly referred to as a case fatality ratio or rate, represents the probability of a disease case being fatal. It is often estimated for various diseases through analysis of surveillance data, case reports, or record examinations. Reported CFR values for Yellow Fever vary, offering wide ranges. Estimates have not been found through systematic literature review, which has been used to estimate CFR of other diseases. This study aims to estimate the case fatality risk of severe Yellow Fever cases through a systematic literature review and meta-analysis. METHODS: A search strategy was implemented in PubMed and Ovid Medline in June 2019 and updated in March 2021, seeking reported severe case counts, defined by fever and either jaundice or hemorrhaging, and the number of those that were fatal. The searches yielded 1,133 studies, and title/abstract review followed by full text review produced 14 articles reporting 32 proportions of fatal cases, 26 of which were suitable for meta-analysis. Four studies with one proportion each were added to include clinical case data from the recent outbreak in Brazil. Data were analyzed through an intercept-only logistic meta-regression with random effects for study. Values of the I2 statistic measured heterogeneity across studies. RESULTS: The estimated CFR was 39 % (95 % CI: 31 %, 47 %). Stratifying by continent showed that South America observed a higher CFR than Africa, though fewer studies reported estimates for South America. No difference was seen between studies reporting surveillance data and studies investigating outbreaks, and no difference was seen among different symptom definitions. High heterogeneity was observed across studies. CONCLUSIONS: Approximately 39 % of severe Yellow Fever cases are estimated to be fatal. This study provides the first systematic literature review to estimate the CFR of Yellow Fever, which can provide insight into outbreak preparedness and estimating underreporting.

Demographic Inequities in Health Outcomes and Air Pollution Exposure in the Atlanta Area and its Relationship to Urban Infrastructure.

Environmental burdens such as air pollution are inequitably distributed with groups of lower socioeconomic statuses, which tend to comprise of large proportions of racial minorities, typically bearing greater exposure. Such groups have also been shown to present more severe health outcomes which can be related to adverse pollution exposure. Air pollution exposure, especially in urban areas, is usually impacted by the built environment, such as major roadways, which can be a significant source of air pollution. This study aims to examine inequities in prevalence of cardiovascular and respiratory diseases in the Atlanta metropolitan region as they relate to exposure to air pollution and characteristics of the built environment. Census tract level data were obtained from multiple sources to model health outcomes (asthma, chronic obstructive pulmonary disease, coronary heart disease, and stroke), pollution exposure (particulate matter and nitrogen oxides), demographics (ethnicity and proportion of elderly residents), and infrastructure characteristics (tree canopy cover, access to green space, and road intersection density). Conditional autoregressive models were fit to the data to account for spatial autocorrelation among census tracts. The statistical model showed areas with majority African-American populations had significantly higher exposure to both air pollutants and higher prevalence of each disease. When considering univariate associations between pollution and health outcomes, the only significant association existed between nitrogen oxides and COPD being negatively correlated. Greater percent tree canopy cover and green space access were associated with higher prevalence of COPD, CHD, and stroke. Overall, in considering health outcomes in connection with pollution exposure infrastructure and ethnic demographics, demographics remained the most significant explanatory variable.

Influenza vaccination allocation in tropical settings under constrained resources.

Influenza virus seasonality, synchronicity, and vaccine supply differ substantially between temperate and tropical settings, and optimal vaccination strategy may differ on this basis. Most national vaccine recommendations focus on high-risk groups, elderly populations, and healthcare workers despite previous analyses demonstrating broad benefits to vaccinating younger high-contact age groups. Here, we parameterized an age-structured non-seasonal asynchronous epidemiological model of influenza virus transmission for a tropical low-income setting. We evaluated timing and age allocation of vaccines across vaccine supplies ranging from 10% to 90% using decade-based age groups. Year-round vaccination was beneficial when comparing to vaccination strategies focused on a particular time of year. When targeting a single age-group for vaccine prioritization, maximum vaccine allocation to the 10-19 high-contact age group minimized annual influenza mortality for all but one vaccine supply. When evaluating across all possible age allocations, optimal strategies always allocated a plurality of vaccines to school-age children (10-19). The converse however was not true as not all strategies allocating a plurality to children aged 10-19 minimized mortality. Allocating a high proportion of vaccine supply to the 10-19 age group is necessary but not sufficient to minimize annual mortality as distribution of remaining vaccine doses to other age groups also needs to be optimized. Strategies focusing on indirect benefits (vaccinating children) showed higher variance in mortality outcomes than strategies focusing on direct benefits (vaccinating the elderly). However, the indirect benefit approaches showed lower mean mortality and lower minimum mortality than vaccination focused on the elderly.

Influenza vaccine allocation in tropical settings under constrained resources.

Influenza virus seasonality, synchronicity, and vaccine supply differ substantially between temperate and tropical settings, and optimal vaccination strategy may differ on this basis. Many national vaccine recommendations focus on high-risk groups, elderly populations, and healthcare workers despite previous analyses demonstrating broad benefits to vaccinating younger high-contact age groups. In this study, we parameterized an age-structured nonseasonal asynchronous epidemiological model of influenza virus transmission for a tropical low-income setting. We evaluated timing and age allocation of vaccines across vaccine supplies ranging from 10 to 90% using decade-based age groups. Year-round vaccination was beneficial when compared with more concentrated annual vaccine distribution. When targeting a single age group for vaccine prioritization, maximum vaccine allocation to the 10-19 high-contact age group minimized annual influenza mortality for all but one vaccine supply. When evaluating across all possible age allocations, optimal strategies always allocated a plurality of vaccines to school-age children (10-19). The converse, however, was not true as not all strategies allocating a plurality to children aged 10-19 minimized mortality. Allocating a high proportion of vaccine supply to the 10-19 age group is necessary but not sufficient to minimize annual mortality as distribution of remaining vaccine doses to other age groups also needs to be optimized. Strategies focusing on indirect benefits (vaccinating children) showed higher variance in mortality outcomes than strategies focusing on direct benefits (vaccinating the elderly). However, the indirect benefit approaches showed a lower mean mortality and a lower minimum mortality than vaccination focused on the elderly.

Role of seasonal importation and genetic drift on selection for drug-resistant genotypes of Plasmodium falciparum in high-transmission settings.

Historically Plasmodium falciparum has followed a pattern of drug resistance first appearing in low-transmission settings before spreading to high-transmission settings. Several features of low-transmission regions are hypothesized as explanations: higher chance of symptoms and treatment seeking, better treatment access, less within-host competition among clones and lower rates of recombination. Here, we test whether importation of drug-resistant parasites is more likely to lead to successful emergence and establishment in low-transmission or high-transmission periods of the same epidemiological setting, using a spatial, individual-based stochastic model of malaria and drug-resistance evolution calibrated for Burkina Faso. Upon controlling for the timing of importation of drug-resistant genotypes and examination of key model variables, we found that drug-resistant genotypes imported during the low-transmission season were (i) more susceptible to stochastic extinction due to the action of genetic drift, and (ii) more likely to lead to establishment of drug resistance when parasites are able to survive early stochastic loss due to drift. This implies that rare importation events are more likely to lead to establishment if they occur during a high-transmission season, but that constant importation (e.g. neighbouring countries with high levels of resistance) may produce a greater risk during low-transmission periods.

Weekly Forecasting of Yellow Fever Occurrence and Incidence via Eco-Meteorological Dynamics.

Yellow Fever (YF), a mosquito-borne disease, requires ongoing surveillance and prevention due to its persistence and ability to cause major epidemics, including one that began in Brazil in 2016. Forecasting based on factors influencing YF risk can improve efficiency in prevention. This study aimed to produce weekly forecasts of YF occurrence and incidence in Brazil using weekly meteorological and ecohydrological conditions. Occurrence was forecast as the probability of observing any cases, and incidence was forecast to represent morbidity if YF occurs. We fit gamma hurdle models, selecting predictors from several meteorological and ecohydrological factors, based on forecast accuracy defined by receiver operator characteristic curves and mean absolute error. We fit separate models for data before and after the start of the 2016 outbreak, forecasting occurrence and incidence for all municipalities of Brazil weekly. Different predictor sets were found to produce most accurate forecasts in each time period, and forecast accuracy was high for both time periods. Temperature, precipitation, and previous YF burden were most influential predictors among models. Minimum, maximum, mean, and range of weekly temperature, precipitation, and humidity contributed to forecasts, with optimal lag times of 2, 6, and 7 weeks depending on time period. Results from this study show the use of environmental predictors in providing regular forecasts of YF burden and producing nationwide forecasts. Weekly forecasts, which can be produced using the forecast model developed in this study, are beneficial for informing immediate preparedness measures.

Benefits of near-universal vaccination and treatment access to manage COVID-19 burden in the United States.

Background: As we enter the fourth year of the COVID-19 pandemic, SARS-CoV-2 infections still cause high morbidity and mortality in the United States. During 2020-2022, COVID-19 was one of the leading causes of death in the United States and by far the leading cause among infectious diseases. Vaccination uptake remains low despite this being an effective burden reducing intervention. The development of COVID-19 therapeutics provides hope for mitigating severe clinical outcomes. This modeling study examines combined strategies of vaccination and treatment to reduce the burden of COVID-19 epidemics over the next decade. Methods: We use a validated mathematical model to evaluate the reduction of incident cases, hospitalized cases, and deaths in the United States through 2033 under various levels of vaccination and treatment coverage. We assume that future seasonal transmission patterns for COVID-19 will be similar to those of influenza virus. We account for the waning of infection-induced immunity and vaccine-induced immunity in a future with stable COVID-19 dynamics. Due to uncertainty in the duration of immunity following vaccination or infection, we consider two exponentially-distributed waning rates, with means of 365 days (one year) and 548 days (1.5 years). We also consider treatment failure, including rebound frequency, as a possible treatment outcome. Results: As expected, universal vaccination is projected to eliminate transmission and mortality. Under current treatment coverage (13.7%) and vaccination coverage (49%), averages of 89,000 annual deaths (548-day waning) and 120,000 annual deaths (365-day waning) are expected by the end of this decade. Annual mortality in the United States can be reduced below 50,000 per year with >81% annual vaccination coverage, and below 10,000 annual deaths with >84% annual vaccination coverage. Universal treatment reduces hospitalizations by 88% and deaths by 93% under current vaccination coverage. A reduction in vaccination coverage requires a comparatively larger increase in treatment coverage in order for hospitalization and mortality levels to remain unchanged. Conclusions: Adopting universal vaccination and universal treatment goals in the United States will likely lead to a COVID-19 mortality burden below 50,000 deaths per year, a burden comparable to that of influenza virus.

Role of Seasonal Importation and Random Genetic Drift on Selection for Drug-Resistant Genotypes of Plasmodium falciparum in High Transmission Settings.

Historically Plasmodium falciparum has followed a pattern of drug resistance first appearing in low transmission settings before spreading to high transmission settings. Several features of low-transmission regions are hypothesized as explanations: higher chance of symptoms and treatment seeking, better treatment access, less within-host competition among clones, and lower rates of recombination. Here, we test whether importation of drug-resistant parasites is more likely to lead to successful emergence and establishment in low-transmission or high-transmission periods of the same epidemiological setting, using a spatial, individual-based stochastic model of malaria and drug-resistance evolution calibrated for Burkina Faso. Upon controlling for the timing of importation of drug-resistant genotypes and examination of key model variables, we found that drug-resistant genotypes imported during the low transmission season were, (1) more susceptible to stochastic extinction due to the action of random genetic drift, and (2) more likely to lead to establishment of drug resistance when parasites are able to survive early stochastic loss due to drift. This implies that rare importation events are more likely to lead to establishment if they occur during a high-transmission season, but that constant importation (e.g., neighboring countries with high levels of resistance) may produce a greater risk during low-transmission periods.