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Background After the first COVID-19 wave caused by the ancestral lineage, the pandemic has been fueled from the continuous emergence of new SARS-CoV-2 variants. Understanding key time-to-event periods for each emerging variant of concern is critical as it can provide insights into the future trajectory of the virus and help inform outbreak preparedness and response planning. Here, we aim to examine how the incubation period, serial interval, and generation time have changed from the ancestral SARS-CoV-2 lineage to different variants of concern. Methods We conducted a systematic review and meta-analysis that synthesized the estimates of incubation period, serial interval, and generation time (both realized and intrinsic) for the ancestral lineage, Alpha, Beta, and Omicron variants of SARS-CoV-2. Results Our study included 280 records obtained from 147 household studies, contact tracing studies, or studies where epidemiological links were known. With each emerging variant, we found a progressive shortening of each of the analyzed key time-to-event periods, although we did not find statistically significant differences between the Omicron subvariants. We found that Omicron BA.1 had the shortest pooled estimates for the incubation period (3.49 days, 95% CI: 3.13–4.86 days), Omicron BA.5 for the serial interval (2.37 days, 95% CI: 1.71–3.04 days), and Omicron BA.1 for the realized generation time (2.99 days, 95% CI: 2.48–3.49 days). Only one estimate for the intrinsic generation time was available for Omicron subvariants: 6.84 days (95% CrI: 5.72–8.60 days) for Omicron BA.1. The ancestral lineage had the highest pooled estimates for each investigated key time-to-event period. We also observed shorter pooled estimates for the serial interval compared to the incubation period across the virus lineages. When pooling the estimates across different virus lineages, we found considerable heterogeneities ( I 2  > 80%; I 2 refers to the percentage of total variation across studies that is due to heterogeneity rather than chance), possibly resulting from heterogeneities between the different study populations (e.g., deployed interventions, social behavior, demographic characteristics). Conclusions Our study supports the importance of conducting contact tracing and epidemiological investigations to monitor changes in SARS-CoV-2 transmission patterns. Our findings highlight a progressive shortening of the incubation period, serial interval, and generation time, which can lead to epidemics that spread faster, with larger peak incidence, and harder to control. We also consistently found a shorter serial interval than incubation period, suggesting that a key feature of SARS-CoV-2 is the potential for pre-symptomatic transmission. These observations are instrumental to plan for future COVID-19 waves.

Background Human contact patterns are a key determinant driving the spread of respiratory infectious diseases. However, the relationship between contact patterns and seasonality as well as their possible association with the seasonality of respiratory diseases is yet to be clarified. Methods We investigated the association between temperature and human contact patterns using data collected through a cross‐sectional diary‐based contact survey in Shanghai, China, between December 24, 2017, and May 30, 2018. We then developed a compartmental model of influenza transmission informed by the derived seasonal trends in the number of contacts and validated it against A(H1N1)pdm09 influenza data collected in Shanghai during the same period. Results We identified a significant inverse relationship between the number of contacts and the seasonal temperature trend defined as a spline interpolation of temperature data (p = 0.003). We estimated an average of 16.4 (95% PrI: 15.1–17.5) contacts per day in December 2017 that increased to an average of 17.6 contacts (95% PrI: 16.5–19.3) in January 2018 and then declined to an average of 10.3 (95% PrI: 9.4–10.8) in May 2018. Estimates of influenza incidence obtained by the compartmental model comply with the observed epidemiological data. The reproduction number was estimated to increase from 1.24 (95% CI: 1.21–1.27) in December to a peak of 1.34 (95% CI: 1.31–1.37) in January. The estimated median infection attack rate at the end of the season was 27.4% (95% CI: 23.7–30.5%). Conclusions Our findings support a relationship between temperature and contact patterns, which can contribute to deepen the understanding of the relationship between social interactions and the epidemiology of respiratory infectious diseases.

Aedes -borne diseases represent a major public health threat and mosquito control operations represent a key line of defense. Improving the real-time awareness of mosquito control authorities by providing reliable forecasts of the relative abundance of mosquito vectors could greatly enhance control efforts. To this aim, we developed an analytical tool that forecasts Aedes aegypti relative abundance 1 to 4 weeks ahead. Forecasts were validated against mosquito surveillance data (2,760 data points) collected over multiple years in four jurisdictions in the US. The symmetric absolute percentage error was in the range 0.43–0.69, and the 90% interquantile range of the forecasts had a coverage of 83–92%. Our forecasts consistently outperformed a reference “naïve” model for all analyzed study sites, forecasting horizon, and for periods with medium/high Ae . aegypti activity. The developed tool can be instrumental to address the need for evidence-based decision making.

Mathematical models have been widely used to study the population dynamics of mosquitoes as well as to test and validate the effectiveness of arbovirus outbreak responses and mosquito control strategies. The objective of this study is to assess the diel activity of mosquitoes in Miami-Dade, Florida, and Brownsville, Texas, the most affected areas during the Zika outbreak in 2016–2017, and to evaluate the effectiveness of simulated adulticide treatments on local mosquito populations. To assess variations in the diel activity patterns, mosquitoes were collected hourly for 96 hours once a month from May through November 2019 in Miami-Dade County, Florida, and Brownsville, Texas. We then performed a PERMANOVA followed by a SIMPER analysis to assess whether the abundance and species richness significantly varies at different hours of the day. Finally, we used a mathematical model to simulate the population dynamics of 5 mosquito vector species and evaluate the effectiveness of the simulated adulticide applications. A total of 14,502 mosquitoes comprising 17 species were collected in Brownsville and 10,948 mosquitoes comprising 19 species were collected in Miami-Dade County. Aedes aegypti was the most common mosquito species collected every hour in both cities and peaking in abundance in the morning and the evening. Our modeling results indicate that the effectiveness of adulticide applications varied greatly depending on the hour of the treatment. In both study locations, 9 PM was the best time for adulticide applications targeting all mosquito vector species; mornings/afternoons (9 AM– 5 PM) yielded low effectiveness, especially for Culex species, while at night (12 AM– 6 AM) the effectiveness was particularly low for Aedes species. Our results indicate that the timing of adulticide spraying interventions should be carefully considered by local authorities based on the ecology of the target mosquito species in the focus area.

Background West Nile virus (WNV) is the most common cause of mosquito-borne disease in the continental USA, with an average of ~1200 severe, neuroinvasive cases reported annually from 2005 to 2021 (range 386–2873). Despite this burden, efforts to forecast WNV disease to inform public health measures to reduce disease incidence have had limited success. Here, we analyze forecasts submitted to the 2022 WNV Forecasting Challenge, a follow-up to the 2020 WNV Forecasting Challenge. Methods Forecasting teams submitted probabilistic forecasts of annual West Nile virus neuroinvasive disease (WNND) cases for each county in the continental USA for the 2022 WNV season. We assessed the skill of team-specific forecasts, baseline forecasts, and an ensemble created from team-specific forecasts. We then characterized the impact of model characteristics and county-specific contextual factors (e.g., population) on forecast skill. Results Ensemble forecasts for 2022 anticipated a season at or below median long-term WNND incidence for nearly all (> 99%) counties. More counties reported higher case numbers than anticipated by the ensemble forecast median, but national caseload (826) was well below the 10-year median (1386). Forecast skill was highest for the ensemble forecast, though the historical negative binomial baseline model and several team-submitted forecasts had similar forecast skill. Forecasts utilizing regression-based frameworks tended to have more skill than those that did not and models using climate, mosquito surveillance, demographic, or avian data had less skill than those that did not, potentially due to overfitting. County-contextual analysis showed strong relationships with the number of years that WNND had been reported and permutation entropy (historical variability). Evaluations based on weighted interval score and logarithmic scoring metrics produced similar results. Conclusions The relative success of the ensemble forecast, the best forecast for 2022, suggests potential gains in community ability to forecast WNV, an improvement from the 2020 Challenge. Similar to the previous challenge, however, our results indicate that skill was still limited with general underprediction despite a relative low incidence year. Potential opportunities for improvement include refining mechanistic approaches, integrating additional data sources, and considering different approaches for areas with and without previous cases. Graphical Abstract

The southern US has a large presence of mosquito vector species for dengue virus (DENV) and experiences thousands of DENV importations every year, which have led to several local outbreaks. Adulticide spraying targeting active mosquitoes is one of the most common insecticide strategies used as a response to an outbreak. The aim of this study is to evaluate the effectiveness of adulticide spraying conducted at different times of the day to curb DENV transmission. Based on unique dataset of Aedes aegypti diel activity patterns in Miami-Dade County, Florida, and Brownsville, Texas, we developed a mechanistic model of DENV transmission, which simulates adulticide spraying interventions. We estimated that spraying adulticide for 14 consecutive days at 7am or 8 pm was highly effective in reducing DENV outbreak probability from 10% in the absence of interventions to 0.1% for Miami-Dade County, and from 7.8 to 0.1% for Brownsville. Moreover, in case of a local outbreak in Miami-Dade County, we estimated the median number of symptomatic infections after the identification of a local outbreak to be reduced from 67.0 (IQR: 25.5–103.0) in the absence of interventions to 1.0 (IQR: 0.0–2.0) when spraying adulticide for 14 consecutive days at 8 pm. In Brownsville, the same intervention is estimated to lead to a decrease from 15.0 (IQR: 7.0–33.0) cases to 1.0 (IQR: 0.0–2.0). Our study highlights the importance of considering diel activity patterns of vector mosquito species in arbovirus preparedness and response planning and provide quantitative evidence to guide the decision-making of mosquito control authorities.

Accurate forecasts can enable more effective public health responses during seasonal influenza epidemics. For the 2021-22 and 2022-23 influenza seasons, 26 forecasting teams provided national and jurisdiction-specific probabilistic predictions of weekly confirmed influenza hospital admissions for one-to-four weeks ahead. Forecast skill is evaluated using the Weighted Interval Score (WIS), relative WIS, and coverage. Six out of 23 models outperform the baseline model across forecast weeks and locations in 2021-22 and 12 out of 18 models in 2022-23. Averaging across all forecast targets, the FluSight ensemble is the 2 most accurate model measured by WIS in 2021-22 and the 5 most accurate in the 2022-23 season. Forecast skill and 95% coverage for the FluSight ensemble and most component models degrade over longer forecast horizons. In this work we demonstrate that while the FluSight ensemble was a robust predictor, even ensembles face challenges during periods of rapid change.

Mathematical, computational, and analytical models have been widely used to improve the understanding infectious disease spread and evaluate the impact on public health. Our aim is to improve upon the state-of-the-art approaches by designing modeling tools that integrate new data sources on human host and pathogen vector behaviors. First, we examined the ecological and environmental drivers of arboviral disease spread to inform mosquito control operations by addressing: i) when mosquito control strategies need to be deployed and ii) how to design mosquito control strategies to prevent and mitigate outbreaks. To understand when control strategies need deployed, we developed a forecasting platform that provides precise and accurate real-time forecasts of Aedes aegypti relative abundance to improve situational awareness. To guide the design of mosquito control strategies, we developed a model accounting for mosquito diel activity to evaluate the entomological and epidemiological effects of adulticide spraying at different times of the day in Miami-Dade County, FL, and Brownsville, TX. We used as a case study. Second, we examined the role of social contact patterns in shaping the spread of respiratory infectious disease by addressing: i) how heterogeneities in contact patterns shape the heterogeneities in infectious diseases, ii) how social contact networks impact the estimation of key transmission parameters, and iii) how changes in contact patterns over time affect epidemic dynamics. To investigate the impact of heterogeneities in contacts on the epidemiology of respiratory infectious diseases, we collected and analyzed primary data on the contact patterns of a representative sample of the US population. To estimate the extent to which social clusters, such as households, determine key epidemiological parameters, we developed a multi-scale model that combines within-host viral dynamics and between-hosts pathogen transmission. To demonstrate how changes in contact patterns over time affect epidemic dynamics, we developed statistical and mathematical models to determine whether contact patterns in Shanghai, China, follow a seasonal trend and their impact on influenza seasonality. Our findings show how integrating new data streams into modeling tools can improve our understanding of the epidemiology of infectious diseases as well as providing actionable insights for public health decision making.

Mathematical models have been widely used to study the population dynamics of mosquitoes as well as to test and validate the effectiveness of arbovirus outbreak responses and mosquito control strategies. The objective of this study is to assess the diel activity of mosquitoes in Miami-Dade, Florida, and Brownsville, Texas, the most affected areas during the Zika outbreak in 2016–2017, and to evaluate the effectiveness of simulated adulticide treatments on local mosquito populations. To assess variations in the diel activity patterns, mosquitoes were collected hourly for 96 hours once a month from May through November 2019 in Miami-Dade County, Florida, and Brownsville, Texas. We then performed a PERMANOVA followed by a SIMPER analysis to assess whether the abundance and species richness significantly varies at different hours of the day. Finally, we used a mathematical model to simulate the population dynamics of 5 mosquito vector species and evaluate the effectiveness of the simulated adulticide applications. A total of 14,502 mosquitoes comprising 17 species were collected in Brownsville and 10,948 mosquitoes comprising 19 species were collected in Miami-Dade County. Aedes aegypti was the most common mosquito species collected every hour in both cities and peaking in abundance in the morning and the evening. Our modeling results indicate that the effectiveness of adulticide applications varied greatly depending on the hour of the treatment. In both study locations, 9 PM was the best time for adulticide applications targeting all mosquito vector species; mornings/afternoons (9 AM– 5 PM) yielded low effectiveness, especially for Culex species, while at night (12 AM– 6 AM) the effectiveness was particularly low for Aedes species. Our results indicate that the timing of adulticide spraying interventions should be carefully considered by local authorities based on the ecology of the target mosquito species in the focus area. Author summary Mathematical models have been widely used to study vector mosquitoes as well as to test the effectiveness of arbovirus outbreak response and mosquito control strategies. However, due to the lack of empirical data, there are no studies focusing on the effectiveness of adulticide applications at different hours of the day to control different mosquito populations. This study leveraged a unique dataset in which approximately 25,000 mosquitoes comprising 19 species were collected hourly in Miami-Dade County, Florida, and Brownsville, Texas. We then developed a mathematical model to simulate the population dynamics of five mosquito vector species to evaluate the effectiveness of adulticide spraying at different times of the day and at different frequencies. Mosquito community composition and abundance varied significantly throughout the day in both Brownsville and Miami-Dade County with more than 10-fold differences during the day. Depending on the target vector species, the application of adulticides at a given hour of the day may lead to drastically different results, although we found some common patterns such as the remarkable effectiveness of interventions performed at 9 PM.