Seasonal Influenza Transmission Research Articles

Epidemiological features of seasonal influenza transmission among 11 climate zones in Chinese Mainland

BackgroundPrevious studies provided some evidence of meteorological factors influence seasonal influenza transmission patterns varying across regions and latitudes. However, research on seasonal influenza activities based on climate zones are still in lack. This study aims to utilize the ecological-based Köppen Geiger climate zones classification system to compare the spatial and temporal epidemiological characteristics of seasonal influenza in Chinese Mainland and assess the feasibility of developing an early warning system.MethodsWeekly influenza cases number from 2014 to 2019 at the county and city level were sourced from China National Notifiable Infectious Disease Report Information System. Epidemic temporal indices, time series seasonality decomposition, spatial modelling theories including Moran’s I and local indicators of spatial association were applied to identify the spatial and temporal patterns of influenza transmission.ResultsAll climate zones had peaks in Winter-Spring season. Arid, desert, cold (BWk) showed up the first peak. Only Tropical, savannah (Aw) and Temperate, dry winter with hot summer (Cwa) zones had unique summer peak. Temperate, no dry season and hot summer (Cfa) zone had highest average incidence rate (IR) at 1.047/100,000. The Global Moran’s I showed that average IR had significant clustered trend (z = 53.69, P < 0.001), with local Moran’s I identified high-high cluster in Cfa and Cwa. IR differed among three age groups between climate zones (0–14 years old: F = 26.80, P < 0.001; 15–64 years old: F = 25.04, P < 0.001; Above 65 years old: F = 5.27, P < 0.001). Age group 0–14 years had highest average IR in Cwa and Cfa (IR = 6.23 and 6.21) with unique dual peaks in winter and spring season showed by seasonality decomposition.ConclusionsSeasonal influenza exhibited distinct spatial and temporal patterns in different climate zones. Seasonal influenza primarily emerged in BWk, subsequently in Cfa and Cwa. Cfa, Cwa and BSk pose high risk for seasonal influenza epidemics. The research finds will provide scientific evidence for developing seasonal influenza early warning system based on climate zones.

Decreased Seasonal Influenza Rates Detected in a Crowdsourced Influenza-Like Illness Surveillance System During the COVID-19 Pandemic: Prospective Cohort Study.

Seasonal respiratory viruses had lower incidence during their 2019-2020 and 2020-2021 seasons, which overlapped with the COVID-19 pandemic. The widespread implementation of precautionary measures to prevent transmission of SARS-CoV-2 has been seen to also mitigate transmission of seasonal influenza. The COVID-19 pandemic also led to changes in care seeking and access. Participatory surveillance systems have historically captured mild illnesses that are often missed by surveillance systems that rely on encounters with a health care provider for detection. This study aimed to assess if a crowdsourced syndromic surveillance system capable of detecting mild influenza-like illness (ILI) also captured the globally observed decrease in ILI in the 2019-2020 and 2020-2021 influenza seasons, concurrent with the COVID-19 pandemic. Flu Near You (FNY) is a web-based participatory syndromic surveillance system that allows participants in the United States to report their health information using a brief weekly survey. Reminder emails are sent to registered FNY participants to report on their symptoms and the symptoms of household members. Guest participants may also report. ILI was defined as fever and sore throat or fever and cough. ILI rates were determined as the number of ILI reports over the total number of reports and assessed for the 2016-2017, 2017-2018, 2018-2019, 2019-2020, and 2020-2021 influenza seasons. Baseline season (2016-2017, 2017-2018, and 2018-2019) rates were compared to the 2019-2020 and 2020-2021 influenza seasons. Self-reported influenza diagnosis and vaccination status were captured and assessed as the total number of reported events over the total number of reports submitted. CIs for all proportions were calculated via a 1-sample test of proportions. ILI was detected in 3.8% (32,239/848,878) of participants in the baseline seasons (2016-2019), 2.58% (7418/287,909) in the 2019-2020 season, and 0.27% (546/201,079) in the 2020-2021 season. Both influenza seasons that overlapped with the COVID-19 pandemic had lower ILI rates than the baseline seasons. ILI decline was observed during the months with widespread implementation of COVID-19 precautions, starting in February 2020. Self-reported influenza diagnoses decreased from early 2020 through the influenza season. Self-reported influenza positivity among ILI cases varied over the observed time period. Self-reported influenza vaccination rates in FNY were high across all observed seasons. A decrease in ILI was detected in the crowdsourced FNY surveillance system during the 2019-2020 and 2020-2021 influenza seasons, mirroring trends observed in other influenza surveillance systems. Specifically, the months within seasons that overlapped with widespread pandemic precautions showed decreases in ILI and confirmed influenza. Concerns persist regarding respiratory pathogens re-emerging with changes to COVID-19 guidelines. Traditional surveillance is subject to changes in health care behaviors. Systems like FNY are uniquely situated to detect disease across disease severity and care seeking, providing key insights during public health emergencies.

Forecasting influenza hospital admissions within English sub-regions using hierarchical generalised additive models

BackgroundSeasonal influenza places a substantial burden annually on healthcare services. Policies during the COVID-19 pandemic limited the transmission of seasonal influenza, making the timing and magnitude of a potential resurgence difficult to ascertain and its impact important to forecast.MethodsWe have developed a hierarchical generalised additive model (GAM) for the short-term forecasting of hospital admissions with a positive test for the influenza virus sub-regionally across England. The model incorporates a multi-level structure of spatio-temporal splines, weekly cycles in admissions, and spatial correlation. Using multiple performance metrics including interval score, coverage, bias, and median absolute error, the predictive performance is evaluated for the 2022-2023 seasonal wave. Performance is measured against autoregressive integrated moving average (ARIMA) and Prophet time series models.ResultsAcross the epidemic phases the hierarchical GAM shows improved performance, at all geographic scales relative to the ARIMA and Prophet models. Temporally, the hierarchical GAM has overall an improved performance at 7 and 14 day time horizons. The performance of the GAM is most sensitive to the flexibility of the smoothing function that measures the national epidemic trend.ConclusionsThis study introduces an approach to short-term forecasting of hospital admissions for the influenza virus using hierarchical, spatial, and temporal components. The methodology was designed for the real time forecasting of epidemics. This modelling framework was used across the 2022-2023 winter for healthcare operational planning by the UK Health Security Agency and the National Health Service in England.

Interactive effect of air pollutant and meteorological factors on seasonal influenza transmission, Shanghai, China

BackgroundPrevious studies have provided evidence of seasonal influenza transmission linked to various meteorological and environmental factors. However, the joint effect from both air pollutant exposure and meteorological factors that modify the seasonal influenza incidence is not well discussed. MethodsData on weekly laboratory-confirmed influenza positive case numbers were obtained from Pudong New Area, Shanghai, China between January 1, 2011 and December 31, 2019. Weekly average weather data (absolute humidity (AH), minimum temperature (Min Temp) and particulate matter <2.5 μm (PM2.5) concentration levels) at the same period were collected from Copernicus Atmosphere Monitoring Services (CAMS).A General additive model (GAM) and distributed lag nonlinear model (DLNM) with Quasi-Poisson likelihood were used to evaluate the independent and interaction effect of weather factors and PM2.5 on seasonal influenza transmission along with lag effects. ResultsOur research showed that both AH and Min Temp had U-shape interactive relationships with PM2.5 at high PM2.5 concentration level. Interaction between PM2.5 and AH had positive relative risk (RR) effect at lag of 5 weeks when AH = 5 g/m3and 15 g/m3, as well as the same lag period when Min Temp = 5 °C and 20 °C. ConclusionOur study found that an interactive effect between air pollutant and weather factors exists and significantly affects influenza transmission. For future studies, lag interactive effects among air pollutant and temperature or humidity should be considered in the evaluation of the environment-influenza association, as such interactions might change the disease response to short-term effects.

The seasonal influenza transmission dynamic and the correlation analysis with meteorology in Beijing

Based on the transmission mechanism of seasonal influenza, this paper establishes a SEIMHRS model with hospital-visiting behavior and periodic transmission rate, and then analyzes the existence and stability of disease-free and endemic periodic solutions theoretically. Taking the epidemic of seasonal influenza during 2013–2018 in Beijing and conducting parameter estimation, we derive its basic reproduction ratio [Formula: see text] is 1.0065. Then we concentrate on the correlation between time-varying transmission rate of influenza and change pattern of three meteorology indices for the first time. The results novelly show that there exists a synchronization phenomenon between the transmission rate and variation pattern of average atmospheric pressure and that there is an anti-synchronism phenomenon between that of the average temperature. Whereas it only shows a normal phase difference with the variation pattern of relative humidity. Finally we advocate emphasis on the effect of variation trend of meteorology on influenza prediction.

Effect of Rapid Urbanization in Mainland China on the Seasonal Influenza Epidemic: Spatiotemporal Analysis of Surveillance Data From 2010 to 2017.

The world is undergoing an unprecedented wave of urbanization. However, the effect of rapid urbanization during the early or middle stages of urbanization on seasonal influenza transmission remains unknown. Since about 70% of the world population live in low-income countries, exploring the impact of urbanization on influenza transmission in urbanized countries is significant for global infection prediction and prevention. The aim of this study was to explore the effect of rapid urbanization on influenza transmission in China. We performed spatiotemporal analyses of province-level influenza surveillance data collected in Mainland China from April 1, 2010, to March 31, 2017. An agent-based model based on hourly human contact-related behaviors was built to simulate the influenza transmission dynamics and to explore the potential mechanism of the impact of urbanization on influenza transmission. We observed persistent differences in the influenza epidemic attack rates among the provinces of Mainland China across the 7-year study period, and the attack rate in the winter waves exhibited a U-shaped relationship with the urbanization rates, with a turning point at 50%-60% urbanization across Mainland China. Rapid Chinese urbanization has led to increases in the urban population density and percentage of the workforce but decreases in household size and the percentage of student population. The net effect of increased influenza transmission in the community and workplaces but decreased transmission in households and schools yielded the observed U-shaped relationship. Our results highlight the complicated effects of urbanization on the seasonal influenza epidemic in China. As the current urbanization rate in China is approximately 59%, further urbanization with no relevant interventions suggests a worrisome increasing future trend in the influenza epidemic attack rate.

Transmission Patterns of Seasonal Influenza in China between 2010 and 2018.

Background Understanding the transmission source, pattern, and mechanism of infectious diseases is essential for targeted prevention and control. Though it has been studied for many years, the detailed transmission patterns and drivers for the seasonal influenza epidemics in China remain elusive. Methods In this study, utilizing a suite of epidemiological and genetic approaches, we analyzed the updated province-level weekly influenza surveillance, sequence, climate, and demographic data between 1 April 2010 and 31 March 2018 from continental China, to characterize detailed transmission patterns and explore the potential initiating region and drivers of the seasonal influenza epidemics in China. Results An annual cycle for influenza A(H1N1)pdm09 and B and a semi-annual cycle for influenza A(H3N2) were confirmed. Overall, the seasonal influenza A(H3N2) virus caused more infection in China and dominated the summer season in the south. The summer season epidemics in southern China were likely initiated in the “Lingnan” region, which includes the three most southern provinces of Hainan, Guangxi, and Guangdong. Additionally, the regions in the south play more important seeding roles in maintaining the circulation of seasonal influenza in China. Though intense human mobility plays a role in the province-level transmission of influenza epidemics on a temporal scale, climate factors drive the spread of influenza epidemics on both the spatial and temporal scales. Conclusion The surveillance of seasonal influenza in the south, especially the “Lingnan” region in the summer, should be strengthened. More broadly, both the socioeconomic and climate factors contribute to the transmission of seasonal influenza in China. The patterns and mechanisms revealed in this study shed light on the precise forecasting, prevention, and control of seasonal influenza in China and worldwide.

Effects of baloxavir and oseltamivir antiviral therapy on the transmission of seasonal influenza in China: A mathematical modeling analysis.

New antiviral influenza treatments can effectively alleviate illness while reducing viral shedding. However, how such effects can translate into lower population infections of seasonal influenza in China remains unknown. To shed light on the public health impacts of novel antiviral agents for influenza, we constructed a dynamic transmission model to simulate the seasonal influenza epidemics in China. Two antivirus treatments, baloxavir and oseltamivir, were evaluated by estimating their impacts on the incidences of influenza infection in a single flu season. In the base-case analysis of a 10% antiviral treatment uptake rate, 2760 and 3420 per 10 000 persons contracted influenza under the treatment of baloxavir and oseltamivir, respectively. These incidence rates amounted to an 18.90% relative risk reduction (RRR) of infection associated with baloxavir in relation to oseltamivir. The corresponding RRR was 82.16% when the antiviral treatment uptake rate was increased to 35%. In addition, the peak of the prevalence of infected individuals per 10 000 persons under the baloxavir treatment was 177 (range: 93-274) fewer than that of oseltamivir. Our analyses suggest that the baloxavir treatment strategy reduces the incidence of influenza in China compared with oseltamivir in the setting of a seasonal flu epidemic. Also, increasing the uptake rate of antiviral treatment can potentially prevent millions of infections during a single flu season.

Was the Reduction in Seasonal Influenza Transmission during 2020 Attributable to Non-Pharmaceutical Interventions to Contain Coronavirus Disease 2019 (COVID-19) in Japan?

We quantified the effects of adherence to various non-pharmaceutical interventions (NPIs) on the seasonal influenza epidemic dynamics in Japan during 2020. The total monthly number of seasonal influenza cases per sentinel site (seasonal influenza activity) reported to the National Epidemiological Surveillance of Infectious Diseases and alternative NPI indicators (retail sales of hand hygiene products and number of airline passenger arrivals) from 2014–2020 were collected. The average number of monthly seasonal influenza cases in 2020 had decreased by approximately 66.0% (p < 0.001) compared to those in the preceding six years. An increase in retail sales of hand hygiene products of ¥1 billion over a 3-month period led to a 15.5% (95% confidence interval [CI]: 10.9–20.0%; p < 0.001) reduction in seasonal influenza activity. An increase in the average of one million domestic and international airline passenger arrivals had a significant association with seasonal influenza activity by 11.6% at lag 0–2 months (95% CI: 6.70–16.5%; p < 0.001) and 30.9% at lag 0–2 months (95% CI: 20.9–40.9%; p < 0.001). NPI adherence was associated with decreased seasonal influenza activity during the COVID-19 pandemic in Japan, which has crucial implications for planning public health interventions to minimize the health consequences of adverse seasonal influenza epidemics.

The effectiveness of governmental nonpharmaceutical interventions against COVID-19 at controlling seasonal influenza transmission: an ecological study

BackgroundA range of strict nonpharmaceutical interventions (NPIs) were implemented in many countries to combat the coronavirus 2019 (COVID-19) pandemic. These NPIs may also be effective at controlling seasonal influenza virus infections, as influenza viruses have the same transmission path as severe acute respiratory syndrome coronavirus 2. The aim of this study was to evaluate the effects of different NPIs on the control of seasonal influenza.MethodsData for 14 NPIs implemented in 33 countries and the corresponding influenza virological surveillance data were collected. The influenza suppression index was calculated as the difference between the influenza positivity rate during its period of decline from 2019 to 2020 and during the influenza epidemic seasons in the previous 9 years. A machine learning model was developed using an extreme gradient boosting tree regressor to fit the NPI and influenza suppression index data. The SHapley Additive exPlanations tool was used to characterize the NPIs that suppressed the transmission of influenza.ResultsOf all NPIs tested, gathering limitations had the greatest contribution (37.60%) to suppressing influenza transmission during the 2019–2020 influenza season. The three most effective NPIs were gathering limitations, international travel restrictions, and school closures. For these three NPIs, their intensity threshold required to generate an effect were restrictions on the size of gatherings less than 1000 people, ban of travel to all regions or total border closures, and closing only some categories of schools, respectively. There was a strong positive interaction effect between mask-wearing requirements and gathering limitations, whereas merely implementing a mask-wearing requirement, and not other NPIs, diluted the effectiveness of mask-wearing requirements at suppressing influenza transmission.ConclusionsGathering limitations, ban of travel to all regions or total border closures, and closing some levels of schools were found to be the most effective NPIs at suppressing influenza transmission. It is recommended that the mask-wearing requirement be combined with gathering limitations and other NPIs. Our findings could facilitate the precise control of future influenza epidemics and other potential pandemics.

Human seasonal influenza under COVID-19 and the potential consequences of influenza lineage elimination

Annual epidemics of seasonal influenza cause hundreds of thousands of deaths, high levels of morbidity, and substantial economic loss. Yet, global influenza circulation has been heavily suppressed by public health measures and travel restrictions since the onset of the COVID-19 pandemic. Notably, the influenza B/Yamagata lineage has not been conclusively detected since April 2020, and A(H3N2), A(H1N1), and B/Victoria viruses have since circulated with considerably less genetic diversity. Travel restrictions have largely confined regional outbreaks of A(H3N2) to South and Southeast Asia, B/Victoria to China, and A(H1N1) to West Africa. Seasonal influenza transmission lineages continue to perish globally, except in these select hotspots, which will likely seed future epidemics. Waning population immunity and sporadic case detection will further challenge influenza vaccine strain selection and epidemic control. We offer a perspective on the potential short- and long-term evolutionary dynamics of seasonal influenza and discuss potential consequences and mitigation strategies as global travel gradually returns to pre-pandemic levels.

Quantifying the Impact of COVID-19 Nonpharmaceutical Interventions on Influenza Transmission in the United States.

BackgroundNonpharmaceutical interventions (NPIs) have been implemented to suppress the transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Evidence indicates that NPIs against COVID-19 may also have effects on the transmission of seasonal influenza.MethodsIn this study, we use an absolute humidity-driven susceptible-infectious-recovered-susceptible (SIRS) model to quantify the reduction of influenza incidence and transmission in the US and HHS (the US Department of Health and Human Services) regions after implementation of NPIs in 2020. We investigate the long-term effect of NPIs on influenza incidence by projecting influenza transmission at the national scale over the next five years, using the SIRS model.ResultsWe estimate that incidence of influenza A/H1 and B, which circulated in early 2020, was reduced by more than 60% in the US during the first ten weeks following implementation of NPIs. The reduction of influenza transmission exhibits clear geographical variation. After the control measures are relaxed, potential accumulation of susceptibility to influenza infection may lead to a large outbreak, the scale of which may be affected by length of the intervention period and duration of immunity to influenza.DiscussionHealthcare systems need to prepare for potential influenza patient surges and advocate vaccination and continued precautions.

Seasonal influenza during the COVID-19 pandemic in Bangladesh.

During the 2019 novel coronavirus infectious disease (COVID-19) pandemic in 2020, limited data from several countries suggested reduced seasonal influenza viruses' circulation. This was due to community mitigation measures implemented to control the pandemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We used sentinel surveillance data to identify changes in the 2020 influenza season compared with previous seasons in Bangladesh. We used hospital-based influenza surveillance (HBIS) data of Bangladesh that are generated year-round and are population-representative severe acute respiratory infection (SARI) data for all age groups from seven public and two private tertiary care level hospitals data from 2016 to 2019. We applied the moving epidemic method (MEM) by using R language (v4.0.3), and MEM web applications (v2.14) on influenza-positive rates of SARI cases collected weekly to estimate an average seasonal influenza curve and establish epidemic thresholds. The 2016-2019 average season started on epi week 18 (95% CI: 15-25) and lasted 12.5 weeks (95% CI: 12-14 weeks) until week 30.5. The 2020 influenza season started on epi week 36 and ended at epi week 41, lasting for only five weeks. Therefore, influenza epidemic started 18 weeks later, was 7.5 weeks shorter, and was less intense than the average epidemic of the four previous years. The 2020 influenza season started on the same week when COVID-19 control measures were halted, and 13 weeks after the measures were relaxed. Our findings suggest that seasonal influenza circulation in Bangladesh was delayed and less intense in 2020 than in previous years. Community mitigation measures may have contributed to this reduction of seasonal influenza transmission. These findings contribute to a limited but growing body of evidence that influenza seasons were altered globally in 2020.

Multi-agent system collision model to predict the transmission of seasonal influenza in Tokyo from 2014–2015 to 2018–2019 seasons

The objective of this study was to apply the multi-agent system (MAS) collision model to predict seasonal influenza epidemic in Tokyo for 5 seasons (2014–2015 to 2018–2019 seasons). The MAS collision model assumes each individual as a particle inside a square domain. The particles move within the domain and disease transmission occurs in a certain probability when an infected particle collides a susceptible particle. The probability was determined based on the basic reproduction number calculated using the actual data. The simulation started with 1 infected particle and 999 susceptible particles to correspond to the onset of an influenza epidemic. We performed the simulation for 150 days and the calculation was repeated 500 times for each season. To improve the accuracy of the prediction, we selected simulations which have similar incidence number to the actual data in specific weeks. Analysis including all simulations corresponded good to the actual data in 2014–2015 and 2015–2016 seasons. However, the model failed to predict the sharp peak incidence after the New Year Holidays in 2016–2017, 2017–2018, and 2018–2019 seasons. A model which included simulations selected by the week of peak incidence predicted the week and number of peak incidence better than a model including all simulations in all seasons. The reproduction number was also similar to the actual data in this model. In conclusion, the MAS collision model predicted the epidemic curve with good accuracy by selecting the simulations using the actual data without changing the initial parameters such as the basic reproduction number and infection time.

Asymptomatic transmission and high community burden of seasonal influenza in an urban and a rural community in South Africa, 2017–18 (PHIRST): a population cohort study

SummaryBackgroundData on influenza community burden and transmission are important to plan interventions especially in resource-limited settings. However, data are limited, particularly from low-income and middle-income countries. We aimed to evaluate the community burden and transmission of influenza in a rural and an urban setting in South Africa.MethodsIn this prospective cohort study approximately 50 households were selected sequentially from both a rural setting (Agincourt, Mpumalanga Province, South Africa; with a health and sociodemographic surveillance system) and an urban setting (Klerksdorp, Northwest Province, South Africa; using global positioning system data), enrolled, and followed up for 10 months in 2017 and 2018. Different households were enrolled in each year. Households of more than two individuals in which 80% or more of the occupants agreed to participate were included in the study. Nasopharyngeal swabs were collected twice per week from participating household members irrespective of symptoms and tested for influenza using real-time RT-PCR. The primary outcome was the incidence of influenza infection, defined as the number of real-time RT-PCR-positive episodes divided by the person-time under observation. Household cumulative infection risk (HCIR) was defined as the number of subsequent infections within a household following influenza introduction.Findings81 430 nasopharyngeal samples were collected from 1116 participants in 225 households (follow-up rate 88%). 917 (1%) tested positive for influenza; 178 (79%) of 225 households had one or more influenza-positive individual. The incidence of influenza infection was 43·6 (95% CI 39·8–47·7) per 100 person-seasons. 69 (17%) of 408 individuals who had one influenza infection had a repeat influenza infection during the same season. The incidence (67·4 per 100 person-seasons) and proportion with repeat infections (22 [23%] of 97 children) were highest in children younger than 5 years and decreased with increasing age (p<0·0001). Overall, 268 (56%) of 478 infections were symptomatic and 66 (14%) of 478 infections were medically attended. The overall HCIR was 10% (109 of 1088 exposed household members infected [95% CI 9–13%). Transmission (HCIR) from index cases was highest in participants aged 1–4 years (16%; 40 of 252 exposed household members) and individuals with two or more symptoms (17%; 68 of 396 exposed household members). Individuals with asymptomatic influenza transmitted infection to 29 (6%) of 509 household contacts. HIV infection, affecting 167 (16%) of 1075 individuals, was not associated with increased incidence or HCIR.InterpretationApproximately half of influenza infections were symptomatic, with asymptomatic individuals transmitting influenza to 6% of household contacts. This suggests that strategies, such as quarantine and isolation, might be ineffective to control influenza. Vaccination of children, with the aim of reducing influenza transmission might be effective in African settings given the young population and high influenza burden.

Estimating the Protectiveness of Face Mask on Seasonal Influenza Transmissibility During COVID-19 Early Spread

Background: After COVID-19 early expansion occurred in mainland China, seasonal influenza transmission in Hong Kong, which was growing initially, immediately plateaued, and then abruptly declined a few weeks later. These patterns correspond to the three phases of early COVID-19 spread from Wuhan to Hong Kong, i.e. the ordinary: before the occurrence in Wuhan; the awareness: after the evidence of human-to-human transmission was revealed; and the spreading: after the first local case was confirmed in Hong Kong. The available surveyed data on changes in precautionary behavior during these phases, i.e. face mask wearing and avoiding the crowd, provide an opportunity to estimate the protectiveness of face mask on influenza transmissibility. Methods: We developed a time-series susceptible-infected-recovered (TS-SIR) regression model to estimate the time-varying effective reproduction number Rt based on the weekly reported influenza cases. The reporting rate of influenza was adjusted under the assumption that patients with severe influenza were seeking medical care. After separating the effect from herd immunity, the percent reduction in Rt from each behavior was calculated as an indication of the protectiveness. Findings: The average Rt of winter influenza season in 2019/20 was estimated in the three phases: 1.29 (95%CI, 1.27 to 1.32) in the ordinary, 1.00 (95%CI, 0.99 to 1.00) in the awareness, and 0.73 (95%CI, 0.73 to 0.74) in the spreading. Our results showed that face mask wearing protected 22% from being transmitted, which was nearly half of the effect of avoiding the crowd (42%). If more than 79% of the people adopted both precautionary behaviors, the initial Rt reduced to less than one. Interpretation: The results suggested that mandatory face mask wearing along with social distancing practices could be effective in suppressing the transmission of influenza, which may also give hints on preventing COVID-19 infection.Funding Information: We declare no competing interests.Declaration of Interests: We acknowledge the support from grants funded by Health and Medical Research Fund [COVID190329415], City University of Hong Kong [7200573], andWellcome Trust and The Royal Society [213494/Z/18/3Z95].

Perinatal Outcomes of Asynchronous Influenza Vaccination, Ceará, Brazil, 2013-2018.

In Ceará, Brazil, seasonal influenza transmission begins before national annual vaccination campaigns commence. To assess the perinatal consequences of this misalignment, we tracked severe acute respiratory infection (SARI), influenza, and influenza immunizations during 2013–2018. Among 3,297 SARI cases, 145 (4.4%) occurred in pregnant women. Statewide vaccination coverage was >80%; however, national vaccination campaigns began during or after peak influenza season. Thirty to forty weeks after peak influenza season, birthweights decreased by 40 g, and rates of prematurity increased from 10.7% to 15.5%. We identified 61 children born to mothers with SARI during pregnancy; they weighed 10% less at birth and were more likely to be premature than 122 newborn controls. Mistiming of influenza vaccination campaigns adversely effects perinatal outcomes in Ceará. Because Ceará is the presumptive starting point for north-to-south seasonal influenza transmission in Brazil, earlier national immunization campaigns would provide greater protection for pregnant women and their fetuses in Ceará and beyond.

Different Transmission Dynamics of Coronavirus Disease 2019 (COVID-19) and Influenza Suggest the Relative Efficiency of Isolation/Quarantine and Social Distancing Against COVID-19 in China

BackgroundNon-pharmaceutical interventions (NPIs) against Coronavirus Disease 2019 (COVID-19) are vital to reducing the transmission risks. However, the relative efficiency of social distancing against COVID-19 remains controversial, since social distancing and isolation/quarantine were implemented almost at the same time in China.MethodsIn this study, surveillance data of COVID-19 and seasonal influenza in the year 2018-2020 were used to quantify the relative efficiency of NPIs against COVID-19 in China, since isolation/quarantine was not used for the influenza epidemics. Given that the relative age-dependent susceptibility to influenza and COVID-19 may vary, an age-structured Susceptible-Infected-Recovered model was built to explore the efficiency of social distancing against COVID-19 under different population susceptibility scenarios.ResultsThe mean effective reproductive number, Rt, of COVID-19 before NPIs was 2.12 (95% confidential interval (CI): 2.02-2.21). By March 11, 2020, the overall reduction in Rt of COVID-19 was 66.1% (95% CI: 60.1%-71.2%). In the epidemiological year 2019/20, influenza transmissibility reduced by 34.6% (95% CI: 31.3%-38.2%) compared with that in the epidemiological year 2018/19. Under the observed contact patterns changes in China, social distancing had similar efficiency against COVID-19 in three different scenarios. By assuming same efficiency of social distancing against seasonal influenza and COVID-19 transmission, isolation/quarantine and social distancing could lead to a 48.1% (95% CI: 35.4%-58.1%) and 34.6% (95% CI: 31.3%-38.2%) reduction of the transmissibility of COVID-19.ConclusionsThough isolation/quarantine is more effective than social distancing, given that typical basic reproductive number of COVID-19 is 2-3, isolation/quarantine alone could not contain the COVID-19 pandemic effectively in China.

Successful interruption of seasonal influenza transmission under the COVID-19 rapid response in Zhejiang Province, China

ObjectivesThe objective of this study was to evaluate whether the non-pharmaceutical interventions (NPIs) introduced to curb the spread of coronavirus disease 2019 (COVID-19) also interrupted the transmission of influenza. Study designThis is a descriptive epidemiological study. MethodsData on changes in the number of reported influenza cases, number of influenza-like illness (ILI) visits, ILI percentage and influenza virus positivity were compared between the first 18 weeks of 2020 and the same period of 2019. ResultsThe changes in the weekly average number of influenza cases were statistically significant between 2020 and 2019 (−4319 vs −525 per week; P < 0.05). The slopes of regression lines for the number of ILI visits were also statistically significant between 2020 and 2019 (−911 vs −98 per week; P < 0.05). ConclusionsThis study found that the prevalence of influenza was substantially decreased when NPIs were implemented for the containment of COVID-19.

Nonpharmaceutical Interventions Used to Control COVID-19 Reduced Seasonal Influenza Transmission in China.

To suppress the ongoing COVID-19 pandemic, the Chinese government has implemented a set of non-pharmaceutical interventions (NPIs). Because COVID-19 and influenza have similar means of transmission, it is hypothesized that NPIs targeting COVID-19 may also affect influenza transmission. In this study, the extent to which NPIs targeting COVID-19 have affected seasonal influenza transmission was explored. Indicators of seasonal influenza activity in the epidemiological year 2019/20 were compared with those in 2017/18 and 2018/19. Results show that the incidence rate of seasonal influenza reduced by 64% in 2019/20 (p&0.001). These findings suggest that NPIs aimed at controlling COVID-19 significantly reduced the seasonal influenza transmission in China. (105 words)