School closure and mitigation of pandemic (H1N1) 2009, Hong Kong

Strategies for Influenza Outbreak Management in a US Residential Summer Camp Communal Housing: A Comparative Analysis of Targeted Antiviral Prophylaxis

BACKGROUND

Previous research has underscored the efficacy of individual control strategies in mitigating influenza spread within communal settings; however, the unique dynamics of residential summer camps-characterized by close contact and high social interaction-present distinct challenges for outbreak management.

PURPOSE

The purpose of this study was to evaluate and compare the effectiveness of two targeted antiviral prophylaxis protocols using oseltamivir in controlling influenza outbreaks within a residential youth camp, aiming to provide evidence-based insights for optimizing outbreak management in communal settings with high social interaction.

DESIGN

This retrospective study analyzed the progression of influenza outbreaks in a residential youth camp using two antiviral prophylaxis protocols with oseltamivir. Time-series models assessed outbreak dynamics, and descriptive statistics characterized camp cohorts to evaluate the effectiveness of mass chemoprophylaxis (2022 Protocol) versus more rapidly deployed chemoprophylaxis (2023 Protocol).

METHODS

We used descriptive statistics to define the camp cohorts and time-series models to analyze the outbreak's progression under each protocol.

FINDINGS

In 2022, oseltamivir was widely distributed after the outbreak began, likely resulting in a reduced but ongoing spread. In 2023, targeted use of oseltamivir early in the outbreak significantly reduced transmission within the camp.

CONCLUSION

The study demonstrated oseltamivir's efficacy in reducing influenza transmission and emphasized the importance of rapid intervention in communal settings, offering valuable insights for optimizing outbreak management.

CLINICAL EVIDENCE

Early intervention with oseltamivir was more effective in controlling the outbreak than a later intervention. Targeted use of oseltamivir, focusing on individuals exposed to the virus, was shown to be beneficial.

Interplay Between Viral Shedding, Age, and Symptoms in Individual Infectiousness of Influenza Cases in Households

BACKGROUND

Understanding factors affecting the infectiousness of influenza cases is crucial for disease prevention and control. Viral shedding is expected to correlate with infectiousness of cases, but it is strongly associated with age and the presence of symptoms.

METHODS

To elucidate this complex interplay, we analyze with an individual-based household transmission model a detailed household transmission study of influenza with 442 households and 1710 individuals from 2008 to 2017 in Hong Kong, to characterize the household transmission dynamics and identify factors affecting transmissions.

RESULTS

We estimate that age, fever symptoms, and viral load were all associated with higher infectiousness. However, by model comparison, the best model included age and fever as factors affecting individual infectiousness, and estimates that preschool and school-aged children were 317% (95% credible interval [CrI], 103%, 1042%) and 161% (95% CrI, 33%, 601%) more infectious than adults, respectively, and patients having fever had 146% (95% CrI, 37%, 420%) higher infectiousness. Adding heterogeneity on individual infectiousness of cases does not improve the model fit, suggesting these factors could explain the difference in individual infectiousness.

CONCLUSIONS

Our study clarifies the contribution of age, symptoms, and viral shedding to individual infectiousness of influenza cases in households.

Evaluating the impact of school-based influenza vaccination programme on absenteeism and outbreaks at schools in Hong Kong: a retrospective cohort study protocol

INTRODUCTION

Seasonal influenza causes annual school breaks and student absenteeism in Hong Kong schools and kindergartens. This proposal aims to conduct a retrospective cohort study to evaluate the impact of a school-based influenza vaccination (SIV) programme on absenteeism and outbreaks at schools in Hong Kong.

METHODS

The study will compare schools that implemented the SIV programme with schools that did not. The data will be sourced from school records, encompassing absenteeism records, outbreak reports, and vaccination rates. We will recruit 1000 students from 381 schools and kindergartens in 18 districts of Hong Kong starting June 2024. The primary outcome measures will include absenteeism rates due to influenza and school influenza outbreaks. Secondary outcomes will consist of vaccination coverage rates and the impact of the SIV programme on hospitalisations due to influenza-like illness. A t-test will be conducted to compare the outcomes between schools with and without the SIV programme.

ETHICS AND DISSEMINATION

The school completed signing the participants' informed consent form before reporting the data to us. Our study has been approved by the Hospital Authority Hong Kong West Cluster IRB Committee (IRB No: UW 17-111) and was a subtopic of the research "The estimated age-group specific influenza vaccine coverage rates in Hong Kong and the impact of the school outreach vaccination program".

TRIAL REGISTRATION

This study will be retrospectively registered.

Effect of planned school breaks on student absenteeism due to influenza-like illness in school aged children-Oregon School District, Wisconsin September 2014-June 2019

Background

School-aged children and school reopening dates have important roles in community influenza transmission. Although many studies evaluated the impact of reactive closures during seasonal and pandemic influenza outbreaks on medically attended influenza in surrounding communities, few assess the impact of planned breaks (i.e., school holidays) that coincide with influenza seasons, while accounting for differences in seasonal peak timing. Here, we analyze the effects of winter and spring breaks on influenza risk in school-aged children, measured by student absenteeism due to influenza-like illness (a-ILI).

Methods

We compared a-ILI counts in the 2-week periods before and after each winter and spring break over five consecutive years in a single school district. We introduced a "pseudo-break" of 9 days' duration between winter and spring break each year when school was still in session to serve as a control. The same analysis was applied to each pseudo-break to support any findings of true impact.

Results

We found strong associations between winter and spring breaks and a reduction in influenza risk, with a nearly 50% reduction in a-ILI counts post-break compared with the period before break, and the greatest impact when break coincided with increased local influenza activity while accounting for possible temporal and community risk confounders.

Conclusions

These findings suggest that brief breaks of in-person schooling, such as planned breaks lasting 9-16 calendar days, can effectively reduce influenza in schools and community spread. Additional analyses investigating the impact of well-timed shorter breaks on a-ILI may determine an optimal duration for brief school closures to effectively suppress community transmission of influenza.

Risk factors for transmission in a COVID-19 cluster infection in a high school in the Republic of Korea

BACKGROUND

This study aimed to examine the scale, characteristics, risk factors, and modes of transmission in a coronavirus disease 2019 (COVID-19) outbreak at a high school in Seoul, Republic of Korea.

METHODS

An epidemiological survey was conducted of 1,118 confirmed cases and close contacts from a COVID-19 outbreak at an educational facility starting on May 31, 2021. In-depth interviews, online questionnaires, flow evaluations, and CCTV analyses were used to devise infection prevention measures. Behavioral and spatial risk factors were identified, and statistical significance was tested.

RESULTS

Among 3rd-year students, there were 33 confirmed COVID-19 cases (9.6%). Students who used a study room in the annex building showed a statistically significant 4.3-fold elevation in their relative risk for infection compared to those who did not use the study room. Moreover, CCTV facial recognition analysis confirmed that 17.8% of 3rd-year students did not wear masks and had the lowest percentage of mask-wearers by grade. The air epidemiological survey conducted in the study room in the annex, which met the 3 criteria for a closed space, confirmed that there was only 10% natural ventilation due to the poor ventilation system.

CONCLUSION

To prevent and manage the spread of COVID-19 in educational facilities, advance measures that consider the size, operation, and resources of each school are crucial. In addition, various survey methodologies should be used in future studies to quickly analyze a wider range of data that can inform an evidence-based quarantine response.

Optimal timing of non-pharmaceutical interventions during an epidemic

In response to the recent outbreak of the SARS-CoV-2 virus governments have aimed to reduce the virus's spread through, inter alia, non-pharmaceutical intervention. We address the question when such measures should be implemented and, once implemented, when to remove them. These issues are viewed through a real-options lens and we develop an SIRD-like continuous-time Markov chain model to analyze a sequence of options: the option to intervene and introduce measures and, after intervention has started, the option to remove these. Measures can be imposed multiple times. We implement our model using estimates from empirical studies and, under fairly general assumptions, our main conclusions are that: (1) measures should be put in place not long after the first infections occur; (2) if the epidemic is discovered when there are many infected individuals already, then it is optimal never to introduce measures; (3) once the decision to introduce measures has been taken, these should stay in place until the number of susceptible or infected members of the population is close to zero; (4) it is never optimal to introduce a tier system to phase-in measures but it is optimal to use a tier system to phase-out measures; (5) a more infectious variant may reduce the duration of measures being in place; (6) the risk of infections being brought in by travelers should be curbed even when no other measures are in place. These results are robust to several variations of our base-case model.

Local travel behaviour under continuing COVID-19 waves- A proxy for pandemic fatigue?

COVID-19 continues to threaten the world. Relaxing local travel behaviours on preventing the spread of COVID-19, may increase the infection risk in subsequent waves of SARS-CoV-2 transmission. In this study, we analysed changes in the travel behaviour of different population groups (adult, child, student, elderly) during four pandemic waves in Hong Kong before January 2021, by 4-billion second-by-second smartcard records of subway. A significant continuous relaxation in human travel behaviour was observed during the four waves of SARS-CoV-2 transmission. Residents sharply reduced their local travel by 51.9%, 50.1%, 27.6%, and 20.5% from the first to fourth pandemic waves, respectively. The population flow in residential areas, workplaces, schools, shopping areas, amusement areas and border areas, decreased on average by 30.3%, 33.5%, 41.9%, 58.1%, 85.4% and 99.6%, respectively, during the pandemic weeks. We also found that many other cities around the world experienced a similar relaxation trend in local travel behaviour, by comparing traffic congestion data during the pandemic with data from the same period in 2019. The quantitative pandemic fatigue in local travel behaviour could help governments partially predicting personal protective behaviours, and thus to suggest more accurate interventions during subsequent waves, especially for highly infectious virus variants such as Omicron.

Effect of hybrid immunity, school reopening, and the Omicron variant on the trajectory of the COVID-19 epidemic in India: a modelling study

Background

The course of the COVID-19 pandemic has been driven by several dynamic behavioral, immunological, and viral factors. We used mathematical modeling to explore how the concurrent reopening of schools, increasing levels of hybrid immunity, and the emergence of the Omicron variant affected the trajectory of the pandemic in India, using Andhra Pradesh (pop: 53 million) as an exemplar Indian state.

Methods

We constructed an age- and contact-structured compartmental model that allows for individuals to proceed through various states depending on whether they have received zero, one, or two doses of the COVID-19 vaccine. We calibrated our model using results from another model (i.e., INDSCI-SIM) as well as available context-specific serosurvey data. The introduction of the Omicron variant is modelled alongside protection gained from hybrid immunity. We predict disease dynamics in the background of hybrid immunity coming from infections and an ongoing vaccination program, given prior levels of seropositivity from earlier waves of infection. We describe the consequences of school reopening on cases across different age-bands, as well as the impact of the Omicron (BA.2) variant.

Findings

We show the existence of an epidemic peak in India that is strongly related to the value of background seroprevalence. As expected, because children were not vaccinated in India, re-opening schools increases the number of cases in children more than in adults, although in all scenarios, the peak number of active hospitalizations was never greater than 0.45 times the corresponding peak in the Delta wave before schools were reopened. We varied the level of infection induced seropositivity in our model and found the height of the peak associated with schools reopening reduced as background infection-induced seropositivity increased from 20% to 40%. At reported values of seropositivity of 64% from representative surveys done in India, no discernible peak was observed. We also explored counterfactual scenarios regarding the effect of vaccination on hybrid immunity. We found that in the absence of vaccination, even at high levels of seroprevalence (>60%), the emergence of the Omicron variant would have resulted in a large rise in cases across all age bands by as much as 1.8 times. We conclude that the presence of high levels of hybrid immunity resulted in fewer cases in the Omicron wave than in the Delta wave.

Interpretation

In India, decreasing prevalence of immunologically naïve individuals of all ages was associated with fewer cases reported once schools were reopened. In addition, hybrid immunity, together with the lower intrinsic severity of disease associated with the Omicron variant, contributed to low reported COVID-19 hospitalizations and deaths.

Funding

World Health Organization, Mphasis.

The Impact of Urbanization and Human Mobility on Seasonal Influenza in Northern China

The intensity of influenza epidemics varies significantly from year to year among regions with similar climatic conditions and populations. However, the underlying mechanisms of the temporal and spatial variations remain unclear. We investigated the impact of urbanization and public transportation size on influenza activity. We used 6-year weekly provincial-level surveillance data of influenza-like disease incidence (ILI) and viral activity in northern China. We derived the transmission potential of influenza for each epidemic season using the susceptible-exposed-infectious-removed-susceptible (SEIRS) model and estimated the transmissibility in the peak period via the instantaneous reproduction number (R_t). Public transport was found to explain approximately 28% of the variance in the seasonal transmission potential. Urbanization and public transportation size explained approximately 10% and 21% of the variance in maximum R_t in the peak period, respectively. For the mean R_t during the peak period, urbanization and public transportation accounted for 9% and 16% of the variance in R_t, respectively. Our results indicated that the differences in the intensity of influenza epidemics among the northern provinces of China were partially driven by urbanization and public transport size. These findings are beneficial for predicting influenza intensity and developing preparedness strategies for the early stages of epidemics.

School Closures in the United States and Severe Respiratory Illnesses in Children: A Normalized Nationwide Sample

OBJECTIVES

To determine the association between nationwide school closures and prevalence of common admission diagnoses in the pediatric critical care unit.

DESIGN

Retrospective cohort study.

SETTING

National database evaluation using the Virtual Pediatric Systems LLC database.

PATIENTS

All patients admitted to the PICU in 81 contributing hospitals in the United States.

MEASUREMENTS AND MAIN RESULTS

Diagnosis categories were determined for all 110,418 patients admitted during the 20-week study period in each year (2018, 2019, and 2020). Admission data were normalized relative to statewide school closure dates for each patient using geographic data. The "before school closure" epoch was defined as 8 weeks prior to school closure, and the "after school closure" epoch was defined as 12 weeks following school closure. For each diagnosis, admission ratios for each study day were calculated by dividing 2020 admissions by 2018-2019 admissions. The 10 most common diagnosis categories were examined. Significant changes in admission ratios were identified for bronchiolitis, pneumonia, and asthma. These changes occurred at 2, 8, and 35 days following school closure, respectively. PICU admissions decreased by 82% for bronchiolitis, 76% for pneumonia, and 76% for asthma. Nonrespiratory diseases such as diabetic ketoacidosis, status epilepticus, traumatic injury, and poisoning/ingestion did not show significant changes following school closure.

CONCLUSIONS

School closures are associated with a dramatic reduction in the prevalence of severe respiratory disease requiring PICU admission. School closure may be an effective tool to mitigate future pandemics but should be balanced with potential academic, economic, mental health, and social consequences.

A Tale of Three Recent Pandemics: Influenza, HIV and SARS-CoV-2

Emerging infectious diseases are one of the main threats to public health, with the potential to cause a pandemic when the infectious agent manages to spread globally. The first major pandemic to appear in the 20th century was the influenza pandemic of 1918, caused by the influenza A H1N1 strain that is characterized by a high fatality rate. Another major pandemic was caused by the human immunodeficiency virus (HIV), that started early in the 20th century and remained undetected until 1981. The ongoing HIV pandemic demonstrated a high mortality and morbidity rate, with discrepant impacts in different regions around the globe. The most recent major pandemic event, is the ongoing pandemic of COVID-19, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which has caused over 5.7 million deaths since its emergence, 2 years ago. The aim of this work is to highlight the main determinants of the emergence, epidemic response and available countermeasures of these three pandemics, as we argue that such knowledge is paramount to prepare for the next pandemic. We analyse these pandemics’ historical and epidemiological contexts and the determinants of their emergence. Furthermore, we compare pharmaceutical and non-pharmaceutical interventions that have been used to slow down these three pandemics and zoom in on the technological advances that were made in the progress. Finally, we discuss the evolution of epidemiological modelling, that has become an essential tool to support public health policy making and discuss it in the context of these three pandemics. While these pandemics are caused by distinct viruses, that ignited in different time periods and in different regions of the globe, our work shows that many of the determinants of their emergence and countermeasures used to halt transmission were common. Therefore, it is important to further improve and optimize such approaches and adapt it to future threatening emerging infectious diseases.

Severe Acute Respiratory Syndrome Coronavirus 2 Infections in Children

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections in children generally have milder presentations, but severe disease can occur in all ages. MIS-C and persistent post-acute COVID-19 symptoms can be experienced by children with previous infection and emphasize the need for infection prevention. Optimal treatment for COVID-19 is not known, and clinical trials should include children to guide therapy. Vaccines are the best tool at preventing infection and severe outcomes of COVID-19. Children suffered disproportionately during the pandemic not only from SARS-CoV-2 infection but because of disruptions to daily life, access to primary care, and worsening income inequalities.

Influenza seasonality and its environmental driving factors in mainland China and Hong Kong

BACKGROUND

Influenza epidemics occur during winter in temperate zones, but have less regular seasonality in the subtropics and tropics. Here we quantified the role of environmental drivers of influenza seasonality in temperate and subtropical China.

METHODS

We used weekly surveillance data on influenza virus activity in mainland China and Hong Kong from 2005 through 2016. We estimated the transmissibility via the instantaneous reproduction number (R_t), a real-time measure of transmissibility, and examined its relationship with different climactic drivers and allowed for the timing of school holidays and the decline in susceptibility in the population as an epidemic progressed. We developed a multivariable regression model for R_t to quantify the contribution of various potential environmental drivers of transmission.

FINDINGS

We found that absolute humidity is a potential driver of influenza seasonality and had a U-shaped association with transmissibility and hence can predict the pattern of influenza virus transmission across different climate zones. Absolute humidity was able to explain up to 15% of the variance in R_t, and was a stronger predictor of R_t across the latitudes. Other climatic drivers including mean daily temperature explained up to 13% of variance in R_t and limited to the locations where the indoor measures of these factors have better indicators of outdoor measures. The non-climatic driver, holiday-related school closures could explain up to 7% of variance in R_t.

INTERPRETATION

A U-shaped association of absolute humidity with influenza transmissibility was able to predict seasonal patterns of influenza virus epidemics in temperate and subtropical locations.

Reconstructing antibody dynamics to estimate the risk of influenza virus infection

For >70 years, a 4-fold or greater rise in antibody titer has been used to confirm influenza virus infections in paired sera, despite recognition that this heuristic can lack sensitivity. Here we analyze with a novel Bayesian model a large cohort of 2353 individuals followed for up to 5 years in Hong Kong to characterize influenza antibody dynamics and develop an algorithm to improve the identification of influenza virus infections. After infection, we estimate that hemagglutination-inhibiting (HAI) titers were boosted by 16-fold on average and subsequently decrease by 14% per year. In six epidemics, the infection risks for adults were 3%-19% while the infection risks for children were 1.6-4.4 times higher than that of younger adults. Every two-fold increase in pre-epidemic HAI titer was associated with 19%-58% protection against infection. Our inferential framework clarifies the contributions of age and pre-epidemic HAI titers to characterize individual infection risk.

Taiwan's Response to Influenza: A Seroepidemiological Evaluation of Policies and Implications for Pandemic Preparedness

OBJECTIVES

To evaluate class suspension and mass vaccination implemented among Taipei schoolchildren during the 2009 influenza pandemic and investigate factors affecting antibody responses.

METHODS

We conducted 2 cohort studies on: (1) 972 schoolchildren from November 2009-March 2010 to evaluate pandemic policies and (2) 935 schoolchildren from November 2011-March 2012 to verify factors in antibody waning. Anti-influenza H1N1pdm09 hemagglutination inhibition antibodies (HI-Ab) were measured from serum samples collected before vaccination, and at 1 and 4 months after vaccination. Factors affecting HI-Ab responses were investigated through logistic regression and generalized estimating equation.

RESULTS

Seroprevalence of H1N1pdm09 before vaccination was significantly higher among schoolchildren who experienced class suspensions than those who did not (59.6% vs 47.5%, p<0.05). Participating in after-school activities (adjusted odds ratio [aOR]=2.47, p=0.047) and having ≥3 hours per week of exercise (aOR=2.86, p=0.019) were significantly correlated with H1N1pdm09 infection. Two doses of the H1N1pdm09 vaccine demonstrated significantly better antibody persistence than 1 dose (HI-Ab geometric mean titer: 132.5 vs 88.6, p=0.047). Vaccine effectiveness after controlling for preexisting immunity was 86% (32%-97%). Exercise ≥3 hours per week and preexisting immunity were significantly associated with antibody waning/maintenance.

CONCLUSIONS

This study is the first to show that exercise and preexisting immunity may affect antibody waning. Further investigation is needed to identify immune correlates of protection.

Characterizing the transmission patterns of seasonal influenza in Italy: lessons from the last decade

BACKGROUND

Despite thousands of influenza cases annually recorded by surveillance systems around the globe, estimating the transmission patterns of seasonal influenza is challenging.

METHODS

We develop an age-structured mathematical model to influenza transmission to analyze ten consecutive seasons (from 2010 to 2011 to 2019-2020) of influenza epidemiological and virological data reported to the Italian surveillance system.

RESULTS

We estimate that 18.4-29.3% of influenza infections are detected by the surveillance system. Influenza infection attack rate varied between 12.7 and 30.5% and is generally larger for seasons characterized by the circulation of A/H3N2 and/or B types/subtypes. Individuals aged 14 years or less are the most affected age-segment of the population, with A viruses especially affecting children aged 0-4 years. For all influenza types/subtypes, the mean effective reproduction number is estimated to be generally in the range 1.09-1.33 (9 out of 10 seasons) and never exceeding 1.41. The age-specific susceptibility to infection appears to be a type/subtype-specific feature.

CONCLUSIONS

The results presented in this study provide insights on type/subtype-specific transmission patterns of seasonal influenza that could be instrumental to fine-tune immunization strategies and non-pharmaceutical interventions aimed at limiting seasonal influenza spread and burden.

Life after COVID-19: Future directions?

The humans’ vulnerability and fragility have been demonstrated during pandemics, and as a community, will need proper preparation. The coronavirus outbreak was first reported at the end of 2019 and declared a pandemic by the World Health Organization. Around the world, the response to the virus outbreak has been different.

The detection, traceability, and the response for different countries have been delayed, causing the overwhelming of the health systems. However, some other nations exercised various strategies to contain the infection’s dissemination and recorded a low number of cases. The different measures taken, including contact tracing, lockdown, case detection, social distancing, and quarantine strategies, helped control the disease’s spreading.

Only time will tell how well the world faced the outbreak. We also suggest the future directions that the global community should take to manage and mitigate the emergency.

Human Influenza Epidemiology

Influenza virus infections are common in people of all ages. Epidemics occur in the winter months in temperate locations and at varying times of the year in subtropical and tropical locations. Most influenza virus infections cause mild and self-limiting disease, and around one-half of all infections occur with a fever. Only a small minority of infections lead to serious disease requiring hospitalization. During epidemics, the rates of influenza virus infections are typically highest in school-age children. The clinical severity of infections tends to increase at the extremes of age and with the presence of underlying medical conditions, and impact of epidemics is greatest in these groups. Vaccination is the most effective measure to prevent infections, and in recent years influenza vaccines have become the most frequently used vaccines in the world. Nonpharmaceutical public health measures can also be effective in reducing transmission, allowing suppression or mitigation of influenza epidemics and pandemics.

COVID-19 pandemic: lessons learned from more than a century of pandemics and current vaccine development for pandemic control

Pandemic dynamics and health care responses are markedly different during the COVID-19 pandemic than in earlier outbreaks. Compared with established infectious disease such as influenza, we currently know relatively little about the origin, reservoir, cross-species transmission and evolution of SARS-CoV-2. Health care services, drug availability, laboratory testing, research capacity and global governance are more advanced than during 20th century pandemics, although COVID-19 has highlighted significant gaps. The risk of zoonotic transmission and an associated new pandemic is rising substantially. COVID-19 vaccine development has been done at unprecedented speed, with the usual sequential steps done in parallel. The pandemic has illustrated the feasibility of this approach and the benefits of a globally coordinated response and infrastructure. Some of the COVID-19 vaccines recently developed or currently in development might offer flexibility or sufficiently broad protection to swiftly respond to antigenic drift or emergence of new coronaviruses. Yet many challenges remain, including the large-scale production of sufficient quantity of vaccines, delivery of vaccines to all countries and ensuring vaccination of relevant age groups. This wide vaccine technology approach will be best employed in tandem with active surveillance for emerging variants or new pathogens using antigen mapping, metagenomics and next generation sequencing.

Increased urbanization reduced the effectiveness of school closures on seasonal influenza epidemics in China

BACKGROUND

School closure is a common mitigation strategy during severe influenza epidemics and pandemics. However, the effectiveness of this strategy remains controversial. In this study, we aimed to explore the effectiveness of school closure on seasonal influenza epidemics in provincial-level administrative divisions (PLADs) with varying urbanization rates in China.

METHODS

This study analyzed influenza surveillance data between 2010 and 2019 provided by the Chinese National Influenza Center. Taking into consideration the climate, this study included a region with 3 adjacent PLADs in Northern China and another region with 4 adjacent PLADs in Southern China. The effect of school closure on influenza transmission was evaluated by the reduction of the effective reproductive number of seasonal influenza during school winter breaks compared with that before school winter breaks. An age-structured Susceptible-Infected-Recovered-Susceptible (SIRS) model was built to model influenza transmission in different levels of urbanization. Parameters were determined using the surveillance data via robust Bayesian method.

RESULTS

Between 2010 and 2019, in the less urbanized provinces: Hebei, Zhejiang, Jiangsu and Anhui, during school winter breaks, the effective reproductive number of seasonal influenza epidemics reduced 14.6% [95% confidential interval (CI): 6.2-22.9%], 9.6% (95% CI: 2.5-16.6%), 7.3% (95% CI: 0.1-14.4%) and 8.2% (95% CI: 1.1-15.3%) respectively. However, in the highly urbanized cities: Beijing, Tianjin and Shanghai, it reduced only 5.2% (95% CI: -0.7-11.2%), 4.1% (95% CI: -0.9-9.1%) and 3.9% (95% CI: -1.6-9.4%) respectively. In China, urbanization is associated with decreased proportion of children and increased social contact. According to the SIRS model, both factors could reduce the impact of school closure on seasonal influenza epidemics, and the proportion of children in the population is thought to be the dominant influencing factor.

CONCLUSIONS

Effectiveness of school closure on the epidemics varies with the age structure in the population and social contact patterns. School closure should be recommended in the low urbanized regions in China in the influenza seasons.

Unequal Opportunity Spreaders: Higher COVID-19 Deaths with Later School Closure in the United States

Mixed evidence on the relationship between school closure and COVID-19 prevalence could reflect focus on large-scale levels of geography, limited ability to address endogeneity, and demographic variation. Using county-level Centers for Disease Control and Prevention (CDC) COVID-19 data through June 15, 2020, two matching strategies address potential heterogeneity: nearest geographic neighbor and propensity scores. Within nearest neighboring pairs in different states with different school closure timing, each additional day from a county's first case until state-ordered school closure is related to 1.5 to 2.4 percent higher cumulative COVID-19 deaths per capita (1,227-1,972 deaths for a county with median population and deaths/capita). Results are consistent using propensity score matching, COVID-19 data from two alternative sources, and additional sensitivity analyses. School closure is more strongly related to COVID-19 deaths in counties with a high concentration of Black or poor residents, suggesting schools play an unequal role in transmission and earlier school closure is related to fewer lives lost in disadvantaged counties.

Independent association between meteorological factors, PM2.5, and seasonal influenza activity in Hangzhou, Zhejiang province, China

BACKGROUND

Due to variations in climatic conditions, the effects of meteorological factors and PM2.5 on influenza activity, particularly in subtropical regions, vary in existing literature. In this study, we examined the relationship between influenza activity, meteorological parameters, and PM2.5 .

METHODS

A total of 20 165 laboratory-confirmed influenza cases in Hangzhou, Zhejiang province, were documented in our dataset and aggregated into weekly counts for downstream analysis. We employed a combination of the quasi-Poisson-generalized additive model and the distributed lag non-linear model to examine the relationship of interest, controlling for long-term trends, seasonal trends, and holidays.

RESULTS

A hockey-stick association was found between absolute humidity and the risk of influenza infections. The overall cumulative adjusted relative risk (ARR) was statistically significant when weekly mean absolute humidity was low (<10 µg/m3 ) and high (>17.5 µg/m3 ). A slightly higher ARR was observed when weekly mean temperature reached over 30.5°C. A statistically significantly higher ARR was observed when weekly mean relative humidity dropped below 67%. ARR increased statistically significantly with increasing rainfall. For PM2.5 , the ARR was marginally statistically insignificant. In brief, high temperature, wet and dry conditions, and heavy rainfall were the major risk factors associated with a higher risk of influenza infections.

CONCLUSIONS

The present study contributes additional knowledge to the understanding of the effects of various environmental factors on influenza activities. Our findings shall be useful and important for the development of influenza surveillance and early warning systems.

Flashback and lessons learnt from history of pandemics before COVID-19

With an increasing frequency of infectious disease outbreaks, the COVID-19 pandemic causing mortality around the world and the threat of similar future events looming large, mankind is faced with the herculean task of counteracting such threats with the best possible strategies and public health decisions. It is key that such decisions should be guided by previous examples of similar health emergencies. Here we review some of the significant infectious disease outbreaks, including epidemics and pandemics occurring worldwide in the past including their impact at population and global levels, unique challenges presented by each and the measures taken by authorities worldwide as well as the crucial lessons each epidemic or pandemic provided. This review highlights that throughout history measures such as contact tracing, quarantine and isolation have been incredibly effective in limiting an outbreak in its severity, thus ensuring accurate information flow to the public is as essential as limiting the spread of misinformation. With global populations rising, surveillance for emerging and re-emerging pathogens will play an immense role in preventing future epidemics or pandemics. And finally that even though for novel strains or pathogens, although vaccines are thought to be an irreplaceable defense, but their development and distribution in time to curb an epidemic has seldom been witnessed and remains an important challenge for the future. Hence, we conclude that looking at these past examples not only highlights the important knowledge gained for the strategies to devise, but also the mistakes that can be avoided in the way forward.

Traffic control and social distancing evidence from COVID-19 in China

We collected COVID-19 epidemiological and epidemic control measures-related data in mainland China during the period January 1 to February 19, 2020, and empirically tested the practical effects of the epidemic control measures implemented in China by applying the econometrics approach. The results show that nationally, both traffic control and social distancing have played an important role in controlling the outbreak of the epidemic, however, neither of the two measures have had a significant effect in low-risk areas. Moreover, the effect of traffic control is more successful than that of social distancing. Both measures complement each other, and their combined effect achieves even better results. These findings confirm the effectiveness of the measures currently in place in China, however, we would like to emphasize that control measures should be more tailored, which implemented according to each specific city’s situation, in order to achieve a better epidemic prevention and control.

Changes in local travel behaviour before and during the COVID-19 pandemic in Hong Kong

COVID-19 threatens the world. Social distancing is a significant factor in determining the spread of this disease, and social distancing is strongly affected by the local travel behaviour of people in large cities. In this study, we analysed the changes in the local travel behaviour of various population groups in Hong Kong, between 1 January and 31 March 2020, by using second-by-second smartcard data obtained from the Mass Transit Railway Corporation (MTRC) system. Due to the pandemic, local travel volume decreased by 43%, 49% and 59% during weekdays, Saturdays and Sundays, respectively. The local travel volumes of adults, children, students and senior citizens decreased by 42%, 86%, 73% and 48%, respectively. The local travel behaviour changes for adults and seniors between non-pandemic and pandemic times were greater than those between weekdays and weekends. The opposite was true for children and students. During the pandemic, the daily commute flow decreased by 42%. Local trips to shopping areas, amusement areas and borders decreased by 42%, 81% and 99%, respectively. The effective reproduction number (R t ) of COVID-19 had the strongest association with daily population use of the MTR 7-8 days earlier.

Psychological impacts of COVID-19 and satisfaction from online classes: disturbance in daily routine and prevalence of depression, stress, and anxiety among students of Pakistan

The present study investigated the (i) socio-demographic predictors of psychological distress, (ii) socio-demographic predictors of satisfaction from online classes, and (iii) the relationship between psychological distress and satisfaction from online classes among university students of Pakistan during the COVID-19 pandemic. An online questionnaire-based survey was conducted. A total of 2220 respondents that was enrolled at the University of the Punjab (PU), University of Management and Technology (UMT), and the University of Central Punjab (UCP) were involved in the current study. Data were collected at a 64% response rate and analyzed with SPSS IBM Version 21.0. Results revealed that approximately 41% of the students were facing severe psychological distress while about 65% were found unsatisfied with online classes. Besides, a linear negative relationship between the independent variable, i.e. psychological distress and the dependent variable, i.e. satisfaction from online classes was found. Therefore, to minimize the level of psychological distress and increase students' satisfaction with online classes it is highly recommended to take precautionary measures by the relevant stakeholders.

Consequences of school closure on access to education: Lessons from the 2013-2016 Ebola pandemic

The COVID-19 pandemic has seen an unprecedented shutdown of society. Among the various safety measures taken, much attention has been given to school closure as a non-pharmaceutical mitigation tool to curb the spread of the disease through ensuring "social" (physical) distancing. Nearly 1.725 billion children in over 95% of countries worldwide have been affected by school closures implemented in April 2020 as the virus continued to spread. In the field of education, policymakers' attention has been directed at keeping students on board through remote learning and addressing the immediate needs of schools upon reopening. The study presented in this article focuses on who remains absent after schools resume. Using publicly available survey data from the USAID Demographic Health Surveys Program and the UNICEF Multiple Indicator Cluster Survey from before and after the 2013-2016 Ebola pandemic in Guinea and Sierra Leone in West Africa, the author examined changes in school enrolment and dropout patterns, with targeted consideration given to traditionally marginalised groups. At the time, schools closed for between seven to nine months in the two countries; this length and intensity makes this Ebola pandemic the only health crisis in the recent past to come close to the pandemic-related school closures experienced in 2020. The author's findings suggest that post-Ebola, youth in the poorest households saw the largest increase in school dropout. Exceeding expected pre-Ebola dropout rates, an additional 17,400 of the poorest secondary-age youth were out of school. This evidence is important for minimising the likely post-COVID-19 expansion in inequality. The author's findings point to the need for sustainable planning that looks beyond the reopening of educational institutions to include comprehensive financial support packages for groups most likely to be affected.

Distance education in COVID-19 pandemic: An evaluation of parent's, child's and teacher's competences

COVID-19 has caused profound changes in various dimensions of people's lives. Education system is one of the areas affected most; and there have been profound changes mainly with regard to teachers, students and parents. The main purpose of this research is to analyse the effects of COVID-19 pandemic on ICT competences and experiences of classroom teachers and parents in various dimensions. Scales were developed to collect data for the research. The reliability of the scale was examined by calculating Cronbach Alpha coefficients; which were .690 and .793 for the Distance Education and Pandemic Scale; respectively. In the second study a total of 1345 people participated in the study, including 841 classroom teachers and 504 parents whose children attending primary schools. The findings of the second study revealed significant differences between teachers and parents. Based on the findings of the current study, following suggestions could be given; both parents and teachers should be informed and educated about ICT usage. Teachers should use digital applications like Web 2.0 tools which will direct them through interactive way of teaching.

Implications of the school-household network structure on SARS-CoV-2 transmission under school reopening strategies in England

In early 2020 many countries closed schools to mitigate the spread of SARS-CoV-2. Since then, governments have sought to relax the closures, engendering a need to understand associated risks. Using address records, we construct a network of schools in England connected through pupils who share households. We evaluate the risk of transmission between schools under different reopening scenarios. We show that whilst reopening select year-groups causes low risk of large-scale transmission, reopening secondary schools could result in outbreaks affecting up to 2.5 million households if unmitigated, highlighting the importance of careful monitoring and within-school infection control to avoid further school closures or other restrictions.

Many countries have closed schools as part of their COVID-19 response. Here, the authors model SARS-CoV-2 transmission on a network of schools and households in England, and find that risk of transmission between schools is lower if primary schools are open than if secondary schools are open.

Estimation of parameters for a humidity-dependent compartmental model of the COVID-19 outbreak

Building an effective and highly usable epidemiology model presents two main challenges: finding the appropriate, realistic enough model that takes into account complex biological, social and environmental parameters and efficiently estimating the parameter values with which the model can accurately match the available outbreak data, provide useful projections. The reproduction number of the novel coronavirus (SARS-CoV-2) has been found to vary over time, potentially being influenced by a multitude of factors such as varying control strategies, changes in public awareness and reaction or, as a recent study suggests, sensitivity to temperature or humidity changes. To take into consideration these constantly evolving factors, the paper introduces a time dynamic, humidity-dependent SEIR-type extended epidemiological model with range-defined parameters. Using primarily the historical data of the outbreak from Northern and Southern Italy and with the help of stochastic global optimization algorithms, we are able to determine a model parameter estimation that provides a high-quality fit to the data. The time-dependent contact rate showed a quick drop to a value slightly below 2. Applying the model for the COVID-19 outbreak in the northern region of Italy, we obtained parameters that suggest a slower shrinkage of the contact rate to a value slightly above 4. These findings indicate that model fitting and validation, even on a limited amount of available data, can provide useful insights and projections, uncover aspects that upon improvement might help mitigate the disease spreading.

"We didn't get much schooling because we were fishing all the time": Potential impacts of irregular school attendance on the spread of epidemics

Objectives

Especially in traditional, rural, and low‐income areas, children attend school irregularly. School‐based interventions are common mitigation strategies for infectious disease epidemics, but if daily attendance is not the norm, the impact of schools on disease spread might be overestimated.

Methods

We use an agent‐based model of an early 20th century Newfoundland community to compare epidemic size and duration in three scenarios: (1) all school‐aged children attend school each weekday, (2) students aged 10–15 have a chance of engaging in adult activities each day, and (3) students aged 10–15 have a chance of being reassigned to adult roles at the start of each simulation and thus never attend school.

Results

As the probability of not attending school increases, epidemics become smaller and peak earlier. The change in final size is larger with permanent reassignment (35% at baseline, 18% at maximum reassignment) than with daily nonattendance (35% vs. 22%). For both scenarios, the peak occurs 3 days earlier with maximum absence compared to the baseline. Benefits extend beyond the reassigned agents, as all school‐aged agents are more likely to escape infection with increasing reassignment, and on average, 3–6 additional agents (2.6%–5.3%) escape infection compared to the baseline.

Conclusions

This study demonstrates that absenteeism can have important impacts on epidemic outcomes. Thus, socioeconomic and other reasons for nonattendance of school, as well as how rates vary in different contexts, must be considered in models predicting epidemic outcomes or evaluating public health interventions in the face of major pandemics.

The educational impact of the Covid-19 rapid response on teachers, students, and families: Insights from British Columbia, Canada

The government's rapid response to the spread of Covid-19 in British Columbia has resulted in drastic and unprecedented changes to the delivery of K-12 education. Using qualitative research methods, including remote in-depth interviews, this article addresses the question: What is the educational impact of the Covid-19 rapid response on teachers, students, and families in the Lower Mainland of British Columbia, Canada? Six themes are discussed, including teacher and family responses to change, vulnerability, transitions, work/home life balance, holistic teaching practices and communication. The article ends with recommendations relating to the communication and implementation of health, care, and educational practices that better attend to vulnerable populations and address social emotional wellness.

Will an outbreak exceed available resources for control? Estimating the risk from invading pathogens using practical definitions of a severe epidemic

Forecasting whether or not initial reports of disease will be followed by a severe epidemic is an important component of disease management. Standard epidemic risk estimates involve assuming that infections occur according to a branching process and correspond to the probability that the outbreak persists beyond the initial stochastic phase. However, an alternative assessment is to predict whether or not initial cases will lead to a severe epidemic in which available control resources are exceeded. We show how this risk can be estimated by considering three practically relevant potential definitions of a severe epidemic; namely, an outbreak in which: (i) a large number of hosts are infected simultaneously; (ii) a large total number of infections occur; and (iii) the pathogen remains in the population for a long period. We show that the probability of a severe epidemic under these definitions often coincides with the standard branching process estimate for the major epidemic probability. However, these practically relevant risk assessments can also be different from the major epidemic probability, as well as from each other. This holds in different epidemiological systems, highlighting that careful consideration of how to classify a severe epidemic is vital for accurate epidemic risk quantification.

Influenza A (H1N1)pdm09 outbreak of unknown source in a Ghanaian senior high school

BACKGROUND

Influenza is an acute viral respiratory tract infection caused by influenza virus and transmitted from person to person. Though usually seasonal in temperate climates, influenza occurs throughout the year in the tropics with outbreaks occurring at irregular intervals. On February 6, 2018, a number of students from a Senior High School (SHS) in Accra reported to a district hospital with cough, fever and other respiratory symptoms. An influenza-like illness (ILI) outbreak was suspected. We investigated to determine the magnitude and source of the outbreak and implement control and preventive measures.

METHODS

We interviewed health workers, staff and students of the school as well as case-patients and reviewed health records to collect data on demographic characteristics, signs and symptoms, date of illness onset and outcome. We defined ILI case as "any person in the SHS with fever (measured axillary temperature of ≥ 37.5 °C or history of fever) and cough with or without sore throat or runny nose from January 21 to February 26, 2018". We conducted active case search to identify more cases and took oropharyngeal samples for laboratory testing. We performed descriptive and inferential analysis by calculating attack rate ratios (ARR) and their exact 95% confidence intervals (CI).

RESULTS

Of the 3160 students, 104 case-patients were recorded from January 25, 2018 to February 13, 2018 (overall attack rate of 3.3%). Mean age of case-patients was 16.1 (±2.3) years with males constituting 71.2% (74/104). Sex specific attack rates were 5.6% (74/1331) and 1.6% (30/1829) for males and females respectively. Compared to females, males were 3.4 times as likely to be ill [ARR =3.4, 95%CI = (2.23-5.15)]. Nine oropharyngeal samples from 17 suspected case-patients tested positive for influenza A (H1N1)pdm09.

CONCLUSION

Outbreak of influenza A (H1N1)pdm09 occurred in a SHS in Accra from January to February, 2018. Even though source of the outbreak could not be determined, prompt case management and health education on hand and personal hygiene as non-pharmacological factors probably contributed to the outbreak control. The outbreak ended with a scheduled mid-term break. This underscores the need for more evidence on the effect of school closure in influenza outbreak control.

Job Tension and Emotional Sensitivity to COVID-19 Public Messaging and Risk Perception

During the COVID-19 pandemic, government social marketing messages support strategies of suppression (often stay-at-home orders or lockdowns) and/or mitigation (through testing, isolation, and tracing). Success at lowering the virus reproduction rate (R₀) depends on social marketing messaging that rapidly changes behaviors. This study explores a potential side effect of a successful antivirus public health messaging campaign, when employees are back at work but the virus threat has not disappeared, that leads to on-the-job stress. The authors surveyed office employees in Shanghai, the People's Republic of China, where a nearly 2-month COVID-19 quarantine ended in late March 2020 and work locations reopened with strong public health messaging to encourage cooperation with continued virus spread suppression strategies-an approach likely to be followed in numerous countries. This study examines the relationship of pandemic public messaging sensitivity with tension and negative emotions on the job. Canonical correlation analysis is used with a sample of 1154 respondents, 4 predictor variables (reference group, self-regulation, media, and risk), and 2 criterion variables (negative emotions and job tension). Results show employees are differentially affected by the pandemic background noise. Those more sensitive to social-level virus risks and more open to reference group influence report increased levels of negative emotions and work tension.

Consequences of physical distancing emanating from the COVID-19 pandemic: An Australian perspective

The sobering reality of the COVID-19 pandemic is that it has brought people together at home at a time when we want them apart in the community. This will bring both benefits and challenges. It will affect people differently based upon their age, health status, resilience, family support structures, and socio-economic background. This article will assess the impact in high income countries like Australia, where the initial wave of infection placed the elderly at the greatest risk of death whilst the protective measures of physical distancing, self-isolation, increased awareness of hygiene practices, and school closures with distance learning has had considerable impact on children and families acutely and may have ramifications for years to come.

A simple approach to estimate the instantaneous case fatality ratio: Using the publicly available COVID-19 surveillance data in Canada as an example

The case fatality ratio (CFR) is one of the key measurements to evaluate the clinical severity of infectious diseases. The CFR may vary due to change in factors that affect the mortality risk. In this study, we developed a simple likelihood-based framework to estimate the instantaneous CFR of infectious diseases. We used the publicly available COVID-19 surveillance data in Canada for demonstration. We estimated the mean fatality ratio of reported COVID-19 cases (rCFR) in Canada was estimated at 6.9% (95%CI: 4.5-10.6). We emphasize the extensive implementation of the constructed instantaneous CFR that is to identify the key determinants affecting the mortality risk.

Effects of School Holidays on Seasonal Influenza in South Korea, 2014-2016

School closures are considered as a potential nonpharmaceutical intervention to mitigate severe influenza epidemics and pandemics. In this study, we assessed the effects of scheduled school closure on influenza transmission using influenza surveillance data before, during, and after spring breaks in South Korea, 2014-2016. During the spring breaks, influenza transmission was reduced by 27%-39%, while the overall reduction in transmissibility was estimated to be 6%-23%, with greater effects observed among school-aged children.

Simulating the effect of school closure during COVID-19 outbreaks in Ontario, Canada

BACKGROUND

The province of Ontario, Canada, has instituted indefinite school closures (SC) as well as other social distancing measures to mitigate the impact of the novel coronavirus disease 2019 (COVID-19) pandemic. We sought to evaluate the effect of SC on reducing attack rate and the need for critical care during COVID-19 outbreaks, while considering scenarios with concurrent implementation of self-isolation (SI) of symptomatic cases.

METHODS

We developed an age-structured agent-based simulation model and parameterized it with the demographics of Ontario stratified by age and the latest estimates of COVID-19 epidemiologic characteristics. Disease transmission was simulated within and between different age groups by considering inter- and intra-group contact patterns. The effect of SC of varying durations on the overall attack rate, magnitude and peak time of the outbreak, and requirement for intensive care unit (ICU) admission in the population was estimated. Secondly, the effect of concurrent community-based voluntary SI of symptomatic COVID-19 cases was assessed.

RESULTS

SC reduced attack rates in the range of 7.2-12.7% when the duration of SC increased from 3 to 16 weeks, when contacts among school children were restricted by 60-80%, and in the absence of SI by mildly symptomatic persons. Depending on the scenario, the overall reduction in ICU admissions attributed to SC throughout the outbreak ranged from 3.3 to 6.7%. When SI of mildly symptomatic persons was included and practiced by 20%, the reduction of attack rate and ICU admissions exceeded 6.3% and 9.1% (on average), respectively, in the corresponding scenarios.

CONCLUSION

Our results indicate that SC may have limited impact on reducing the burden of COVID-19 without measures to interrupt the chain of transmission during both pre-symptomatic and symptomatic stages. While highlighting the importance of SI, our findings indicate the need for better understanding of the epidemiologic characteristics of emerging diseases on the effectiveness of social distancing measures.

Projected geographic disparities in healthcare worker absenteeism from COVID-19 school closures and the economic feasibility of child care subsidies: a simulation study

BACKGROUND

School closures have been enacted as a measure of mitigation during the ongoing coronavirus disease 2019 (COVID-19) pandemic. It has been shown that school closures could cause absenteeism among healthcare workers with dependent children, but there remains a need for spatially granular analyses of the relationship between school closures and healthcare worker absenteeism to inform local community preparedness.

METHODS

We provide national- and county-level simulations of school closures and unmet child care needs across the USA. We develop individual simulations using county-level demographic and occupational data, and model school closure effectiveness with age-structured compartmental models. We perform multivariate quasi-Poisson ecological regressions to find associations between unmet child care needs and COVID-19 vulnerability factors.

RESULTS

At the national level, we estimate the projected rate of unmet child care needs for healthcare worker households to range from 7.4 to 8.7%, and the effectiveness of school closures as a 7.6% and 8.4% reduction in fewer hospital and intensive care unit (ICU) beds, respectively, at peak demand when varying across initial reproduction number estimates by state. At the county level, we find substantial variations of projected unmet child care needs and school closure effects, 9.5% (interquartile range (IQR) 8.2-10.9%) of healthcare worker households and 5.2% (IQR 4.1-6.5%) and 6.8% (IQR 4.8-8.8%) reduction in fewer hospital and ICU beds, respectively, at peak demand. We find significant positive associations between estimated levels of unmet child care needs and diabetes prevalence, county rurality, and race (p<0.05). We estimate costs of absenteeism and child care and observe from our models that an estimated 76.3 to 96.8% of counties would find it less expensive to provide child care to all healthcare workers with children than to bear the costs of healthcare worker absenteeism during school closures.

CONCLUSIONS

School closures are projected to reduce peak ICU and hospital demand, but could disrupt healthcare systems through absenteeism, especially in counties that are already particularly vulnerable to COVID-19. Child care subsidies could help circumvent the ostensible trade-off between school closures and healthcare worker absenteeism.

Dynamic interactions of influenza viruses in Hong Kong during 1998-2018

Influenza epidemics cause substantial morbidity and mortality every year worldwide. Currently, two influenza A subtypes, A(H1N1) and A(H3N2), and type B viruses co-circulate in humans and infection with one type/subtype could provide cross-protection against the others. However, it remains unclear how such ecologic competition via cross-immunity and antigenic mutations that allow immune escape impact influenza epidemic dynamics at the population level. Here we develop a comprehensive model-inference system and apply it to study the evolutionary and epidemiological dynamics of the three influenza types/subtypes in Hong Kong, a city of global public health significance for influenza epidemic and pandemic control. Utilizing long-term influenza surveillance data since 1998, we are able to estimate the strength of cross-immunity between each virus-pairs, the timing and frequency of punctuated changes in population immunity in response to antigenic mutations in influenza viruses, and key epidemiological parameters over the last 20 years including the 2009 pandemic. We find evidence of cross-immunity in all types/subtypes, with strongest cross-immunity from A(H1N1) against A(H3N2). Our results also suggest that A(H3N2) may undergo antigenic mutations in both summers and winters and thus monitoring the virus in both seasons may be important for vaccine development. Overall, our study reveals intricate epidemiological interactions and underscores the importance of simultaneous monitoring of population immunity, incidence rates, and viral genetic and antigenic changes.

Nonpharmaceutical Measures for Pandemic Influenza in Nonhealthcare Settings-International Travel-Related Measures

International travel–related nonpharmaceutical interventions (NPIs), which can include traveler screening, travel restrictions, and border closures, often are included in national influenza pandemic preparedness plans. We performed systematic reviews to identify evidence for their effectiveness. We found 15 studies in total. Some studies reported that NPIs could delay the introduction of influenza virus. However, no available evidence indicated that screening of inbound travelers would have a substantial effect on preventing spread of pandemic influenza, and no studies examining exit screening were found. Some studies reported that travel restrictions could delay the start of local transmission and slow international spread, and 1 study indicated that small Pacific islands were able to prevent importation of pandemic influenza during 1918–19 through complete border closure. This limited evidence base indicates that international travel-related NPIs would have limited effectiveness in controlling pandemic influenza and that these measures require considerable resources to implement.

Nonpharmaceutical Measures for Pandemic Influenza in Nonhealthcare Settings-Social Distancing Measures

Influenza virus infections are believed to spread mostly by close contact in the community. Social distancing measures are essential components of the public health response to influenza pandemics. The objective of these mitigation measures is to reduce transmission, thereby delaying the epidemic peak, reducing the size of the epidemic peak, and spreading cases over a longer time to relieve pressure on the healthcare system. We conducted systematic reviews of the evidence base for effectiveness of multiple mitigation measures: isolating ill persons, contact tracing, quarantining exposed persons, school closures, workplace measures/closures, and avoiding crowding. Evidence supporting the effectiveness of these measures was obtained largely from observational studies and simulation studies. Voluntary isolation at home might be a more feasible social distancing measure, and pandemic plans should consider how to facilitate this measure. More drastic social distancing measures might be reserved for severe pandemics.

Projected geographic disparities in healthcare worker absenteeism from COVID-19 school closures and the economic feasibility of child care subsidies: a simulation study

Background

School closures have been enacted as a measure of mitigation during the ongoing COVID-19 pandemic. It has been shown that school closures could cause absenteeism amongst healthcare workers with dependent children, but there remains a need for spatially granular analyses of the relationship between school closures and healthcare worker absenteeism to inform local community preparedness.

Methods

We provide national- and county-level simulations of school closures and unmet child care needs across the United States. We develop individual simulations using county-level demographic and occupational data, and model school closure effectiveness with age-structured compartmental models. We perform multivariate quasi-Poisson ecological regressions to find associations between unmet child care needs and COVID-19 vulnerability factors.

Results

At the national level, we estimate the projected rate of unmet child care needs for healthcare worker households to range from 7.5% to 8.6%, and the effectiveness of school closures to range from 3.2% (R0 = 4) to 7.2% (R0 = 2) reduction in fewer ICU beds at peak demand. At the county-level, we find substantial variations of projected unmet child care needs and school closure effects, ranging from 1.9% to 18.3% of healthcare worker households and 5.7% to 8.8% reduction in fewer ICU beds at peak demand (R0 = 2). We find significant positive associations between estimated levels of unmet child care needs and diabetes prevalence, county rurality, and race (p < 0.05). We estimate costs of absenteeism and child care and observe from our models that an estimated 71.1% to 98.8% of counties would find it less expensive to provide child care to all healthcare workers with children than to bear the costs of healthcare worker absenteeism during school closures.

Conclusions

School closures are projected to reduce peak ICU bed demand, but could disrupt healthcare systems through absenteeism, especially in counties that are already particularly vulnerable to COVID-19. Child care subsidies could help circumvent the ostensible tradeoff between school closures and healthcare worker absenteeism.

Efficient Real-Time Monitoring of an Emerging Influenza Pandemic: How Feasible?

A prompt public health response to a new epidemic relies on the ability to monitor and predict its evolution in real time as data accumulate. The 2009 A/H1N1 outbreak in the UK revealed pandemic data as noisy, contaminated, potentially biased and originating from multiple sources. This seriously challenges the capacity for real-time monitoring. Here, we assess the feasibility of real-time inference based on such data by constructing an analytic tool combining an age-stratified SEIR transmission model with various observation models describing the data generation mechanisms. As batches of data become available, a sequential Monte Carlo (SMC) algorithm is developed to synthesise multiple imperfect data streams, iterate epidemic inferences and assess model adequacy amidst a rapidly evolving epidemic environment, substantially reducing computation time in comparison to standard MCMC, to ensure timely delivery of real-time epidemic assessments. In application to simulated data designed to mimic the 2009 A/H1N1 epidemic, SMC is shown to have additional benefits in terms of assessing predictive performance and coping with parameter nonidentifiability.

Preliminary estimation of the basic reproduction number of novel coronavirus (2019-nCoV) in China, from 2019 to 2020: A data-driven analysis in the early phase of the outbreak

BACKGROUNDS

An ongoing outbreak of a novel coronavirus (2019-nCoV) pneumonia hit a major city in China, Wuhan, December 2019 and subsequently reached other provinces/regions of China and other countries. We present estimates of the basic reproduction number, R0, of 2019-nCoV in the early phase of the outbreak.

METHODS

Accounting for the impact of the variations in disease reporting rate, we modelled the epidemic curve of 2019-nCoV cases time series, in mainland China from January 10 to January 24, 2020, through the exponential growth. With the estimated intrinsic growth rate (γ), we estimated R0 by using the serial intervals (SI) of two other well-known coronavirus diseases, MERS and SARS, as approximations for the true unknown SI.

FINDINGS

The early outbreak data largely follows the exponential growth. We estimated that the mean R0 ranges from 2.24 (95%CI: 1.96-2.55) to 3.58 (95%CI: 2.89-4.39) associated with 8-fold to 2-fold increase in the reporting rate. We demonstrated that changes in reporting rate substantially affect estimates of R0.

CONCLUSION

The mean estimate of R0 for the 2019-nCoV ranges from 2.24 to 3.58, and is significantly larger than 1. Our findings indicate the potential of 2019-nCoV to cause outbreaks.

Preliminary estimation of the basic reproduction number of novel coronavirus (2019-nCoV) in China, from 2019 to 2020: A data-driven analysis in the early phase of the outbreak

BACKGROUNDS

An ongoing outbreak of a novel coronavirus (2019-nCoV) pneumonia hit a major city in China, Wuhan, December 2019 and subsequently reached other provinces/regions of China and other countries. We present estimates of the basic reproduction number, R₀, of 2019-nCoV in the early phase of the outbreak.

METHODS

Accounting for the impact of the variations in disease reporting rate, we modelled the epidemic curve of 2019-nCoV cases time series, in mainland China from January 10 to January 24, 2020, through the exponential growth. With the estimated intrinsic growth rate (γ), we estimated R₀ by using the serial intervals (SI) of two other well-known coronavirus diseases, MERS and SARS, as approximations for the true unknown SI.

FINDINGS

The early outbreak data largely follows the exponential growth. We estimated that the mean R₀ ranges from 2.24 (95%CI: 1.96-2.55) to 3.58 (95%CI: 2.89-4.39) associated with 8-fold to 2-fold increase in the reporting rate. We demonstrated that changes in reporting rate substantially affect estimates of R₀.

CONCLUSION

The mean estimate of R₀ for the 2019-nCoV ranges from 2.24 to 3.58, and is significantly larger than 1. Our findings indicate the potential of 2019-nCoV to cause outbreaks.

Coordinating the real-time use of global influenza activity data for better public health planning

Health planners from global to local levels must anticipate year-to-year and week-to-week variation in seasonal influenza activity when planning for and responding to epidemics to mitigate their impact. To help with this, countries routinely collect incidence of mild and severe respiratory illness and virologic data on circulating subtypes and use these data for situational awareness, burden of disease estimates and severity assessments. Advanced analytics and modelling are increasingly used to aid planning and response activities by describing key features of influenza activity for a given location and generating forecasts that can be translated to useful actions such as enhanced risk communications, and informing clinical supply chains. Here, we describe the formation of the Influenza Incidence Analytics Group (IIAG), a coordinated global effort to apply advanced analytics and modelling to public influenza data, both epidemiological and virologic, in real-time and thus provide additional insights to countries who provide routine surveillance data to WHO. Our objectives are to systematically increase the value of data to health planners by applying advanced analytics and forecasting and for results to be immediately reproducible and deployable using an open repository of data and code. We expect the resources we develop and the associated community to provide an attractive option for the open analysis of key epidemiological data during seasonal epidemics and the early stages of an influenza pandemic.