Social contact patterns relevant to the spread of respiratory infectious diseases in Hong Kong

Reconstructing the age-structured case count of COVID-19 from sentinel surveillance data in Japan: A modeling study

OBJECTIVES

To reconstruct age-structured case counts of COVID-19 using sentinel reporting, which replaced universal reporting of COVID-19 from May 2023 in Japan.

METHODS

Using COVID-19 sentinel data stratified by discrete age groups in selected prefectures and referring to universal case count data up to May 8, 2023, we fitted a statistical model to handle weekly growth rates as a function of age and time so as to convert sentinel data to case counts after cessation of universal reporting.

RESULTS

The age distribution of cases in sentinel reporting was significantly biased toward younger age groups compared to universal reporting. When comparing the epidemic size of the 9th wave (May 8 to September 18, 2023) to the 8th wave (October 3, 2022 to April 10, 2023), using the wave-on-wave ratio of total cumulative sentinel cases led to a significant underestimation of the wave-on-wave in Tokyo (0.975, vs 1.461 by universal reporting) and Okinawa (1.299, vs 1.472). The estimates of growth rates, scaling factors between universal and sentinel cases, and expected universal case count showed robustness to changes in the ending week of the data period.

CONCLUSION

Our model quantified COVID-19 dynamics, comparably to universal reporting that ended in May 2023, enabling detailed and up-to-date health burden analysis using sentinel reports. The cumulative incidence was greater than that suggested from sentinel data in Tokyo, Nara, and Okinawa. Per-population burdens among children were particularly high in Osaka and Nara, indicating a strong bias in sentinel reporting toward pediatric cases.

Stratified epidemic model using a latent marked Hawkes process

We extend the unstructured homogeneously mixing epidemic model introduced by Lamprinakou et al. (2023) to a finite population stratified by age bands. We model the actual unobserved infections using a latent marked Hawkes process and the reported aggregated infections as random quantities driven by the underlying Hawkes process. We apply a Kernel Density Particle Filter (KDPF) to infer the marked counting process, the instantaneous reproduction number for each age group and forecast the epidemic's trajectory in the near future. Taking into account the individual inhomogeneity in age does not increase significantly the computational cost of the proposed inference algorithm compared to the cost of the proposed algorithm for the homogeneously unstructured epidemic model. We demonstrate that considering the individual heterogeneity in age, we can derive the instantaneous reproduction numbers per age group that provide a real-time measurement of interventions and behavioural changes of the associated groups. We illustrate the performance of the proposed inference algorithm on synthetic data sets and COVID-19-reported cases in various local authorities in the UK, and benchmark our model to the unstructured homogeneously mixing epidemic model. Our paper is a "demonstration" of a methodology that might be applied to factors other than age for stratification.

Epidemic spreading on mixing group with face-to-face interaction

The mixing groups gathered in the enclosed space form a complex contact network due to face-to-face interaction, which affects the status and role of different groups in social communication. The intricacies of epidemic spreading in mixing groups are intrinsically complicated. Multiple interactions and transmission add to the difficulties of understanding and forecasting the spread of infectious diseases in mixing groups. Despite the critical relevance of face-to-face interactions in real-world situations, there is a significant lack of comprehensive study addressing the unique issues of mixed groups, particularly those with complex face-to-face interactions. We introduce a novel model employing an agent-based approach to elucidate the nuances of face-to-face interactions within mixing groups. In this paper, we apply a susceptible-infected-susceptible process to mixing groups and integrate a temporal network within a specified time window to distinguish between individual movement patterns and epidemic spreading dynamics. Our findings highlight the significant impact of both the relative size of mixing groups and the groups' mixing patterns on the trajectory of disease spread within the mixing groups. When group sizes differ significantly, high inter-group contact preference limits disease spread. However, if the minority reduces their intra-group preferences while the majority maintains high inter-group contact, disease spread increases. In balanced group sizes, high intra-group contact preferences can limit transmission, but asymmetrically reducing any group's intra-group preference can lead to increased spread.

The contribution of SARS-CoV-2 to the burden of acute respiratory infections in winter season 2022/2023: results from the DigiHero study

OBJECTIVES

In winter of 2022/2023 SARS-CoV-2 had developed into one of many seasonal respiratory pathogens, causing an additional burden of acute respiratory infections (ARIs). Although testing was still widely used, many positive tests were not reported for the official statistics. Using data from a population-based cohort, we aimed to investigate the contribution of SARS-CoV-2 to the burden of ARI.

METHODS

Over 70,000 participants of the German population-based DigiHero study were invited to a questionnaire about the number and time point of ARI and SARS-CoV-2 test results in winter 2022/2023. We calculated the incidence of non-severe acute respiratory syndrome (SARS) ARI, the additional contribution of SARS-CoV-2, and extrapolated the age-specific estimates to obtain the total burden of SARS-CoV-2 in Germany.

RESULTS

For the winter of 2022/2023, 37,708 participants reported 54,813 ARIs, including 9358 SARS-CoV-2 infections. This translated into a cumulative incidence of 145 infections/100 persons for all ARIs, 120 infections/100 persons for non-SARS ARI, and 25 infections/100 persons for SARS ARI (+21%).

CONCLUSIONS

Our estimate for ARI related to SARS-CoV-2 is consistent with the difference in all ARI between pre-pandemic years and 2022/2023. This additional burden should be considered, particularly, with respect to the implications for the work force.

Forecasting of influenza activity and associated hospital admission burden and estimating the impact of COVID-19 pandemic on 2019/20 winter season in Hong Kong

Like other tropical and subtropical regions, influenza viruses can circulate year-round in Hong Kong. However, during the COVID-19 pandemic, there was a significant decrease in influenza activity. The objective of this study was to retrospectively forecast influenza activity during the year 2020 and assess the impact of COVID-19 public health social measures (PHSMs) on influenza activity and hospital admissions in Hong Kong. Using weekly surveillance data on influenza virus activity in Hong Kong from 2010 to 2019, we developed a statistical modeling framework to forecast influenza virus activity and associated hospital admissions. We conducted short-term forecasts (1-4 weeks ahead) and medium-term forecasts (1-13 weeks ahead) for the year 2020, assuming no PHSMs were implemented against COVID-19. We estimated the reduction in transmissibility, peak magnitude, attack rates, and influenza-associated hospitalization rate resulting from these PHSMs. For short-term forecasts, mean ambient ozone concentration and school holidays were found to contribute to better prediction performance, while absolute humidity and ozone concentration improved the accuracy of medium-term forecasts. We observed a maximum reduction of 44.6% (95% CI: 38.6% - 51.9%) in transmissibility, 75.5% (95% CI: 73.0% - 77.6%) in attack rate, 41.5% (95% CI: 13.9% - 55.7%) in peak magnitude, and 63.1% (95% CI: 59.3% - 66.3%) in cumulative influenza-associated hospitalizations during the winter-spring period of the 2019/2020 season in Hong Kong. The implementation of PHSMs to control COVID-19 had a substantial impact on influenza transmission and associated burden in Hong Kong. Incorporating information on factors influencing influenza transmission improved the accuracy of our predictions.

Comprehensive profiling of social mixing patterns in resource poor countries: A mixed methods research protocol

BACKGROUND

Low-and-middle-income countries (LMICs) bear a disproportionate burden of communicable diseases. Social interaction data inform infectious disease models and disease prevention strategies. The variations in demographics and contact patterns across ages, cultures, and locations significantly impact infectious disease dynamics and pathogen transmission. LMICs lack sufficient social interaction data for infectious disease modeling.

METHODS

To address this gap, we will collect qualitative and quantitative data from eight study sites (encompassing both rural and urban settings) across Guatemala, India, Pakistan, and Mozambique. We will conduct focus group discussions and cognitive interviews to assess the feasibility and acceptability of our data collection tools at each site. Thematic and rapid analyses will help to identify key themes and categories through coding, guiding the design of quantitative data collection tools (enrollment survey, contact diaries, exit survey, and wearable proximity sensors) and the implementation of study procedures. We will create three age-specific contact matrices (physical, nonphysical, and both) at each study site using data from standardized contact diaries to characterize the patterns of social mixing. Regression analysis will be conducted to identify key drivers of contacts. We will comprehensively profile the frequency, duration, and intensity of infants' interactions with household members using high resolution data from the proximity sensors and calculating infants' proximity score (fraction of time spent by each household member in proximity with the infant, over the total infant contact time) for each household member.

DISCUSSION

Our qualitative data yielded insights into the perceptions and acceptability of contact diaries and wearable proximity sensors for collecting social mixing data in LMICs. The quantitative data will allow a more accurate representation of human interactions that lead to the transmission of pathogens through close contact in LMICs. Our findings will provide more appropriate social mixing data for parameterizing mathematical models of LMIC populations. Our study tools could be adapted for other studies.

Importance of social inequalities to contact patterns, vaccine uptake, and epidemic dynamics

Individuals' socio-demographic and economic characteristics crucially shape the spread of an epidemic by largely determining the exposure level to the virus and the severity of the disease for those who got infected. While the complex interplay between individual characteristics and epidemic dynamics is widely recognised, traditional mathematical models often overlook these factors. In this study, we examine two important aspects of human behaviour relevant to epidemics: contact patterns and vaccination uptake. Using data collected during the COVID-19 pandemic in Hungary, we first identify the dimensions along which individuals exhibit the greatest variation in their contact patterns and vaccination uptake. We find that generally higher socio-economic groups of the population have a higher number of contacts and a higher vaccination uptake with respect to disadvantaged groups. Subsequently, we propose a data-driven epidemiological model that incorporates these behavioural differences. Finally, we apply our model to analyse the fourth wave of COVID-19 in Hungary, providing valuable insights into real-world scenarios. By bridging the gap between individual characteristics and epidemic spread, our research contributes to a more comprehensive understanding of disease dynamics and informs effective public health strategies.

Social contacts patterns relevant to the transmission of infectious diseases in Suzhou, China following the COVID-19 epidemic

BACKGROUND

The COVID-19 pandemic has profoundly affected human social contact patterns, but there is limited understanding regarding the post-pandemic social contact patterns. Our objective is to quantitatively assess social contact patterns in Suzhou post-COVID-19.

METHODS

We employed a diary design and conducted social contact surveys from June to October 2023, utilizing paper questionnaires. A generalized linear model was utilized to analyze the relationship between individual contacts and covariates. We examined the proportions of contact type, location, duration, and frequency. Additionally, age-related mixed matrices were established.

RESULTS

The participants reported an average of 11.51 (SD 5.96) contact numbers and a total of 19.78 (SD 20.94) contact numbers per day, respectively. The number of contacts was significantly associated with age, household size, and the type of week. Compared to the 0-9 age group, those in the 10-19 age group reported a higher number of contacts (IRR = 1.12, CI: 1.01-1.24), while participants aged 20 and older reported fewer (IRR range: 0.54-0.67). Larger households (5 or more) reported more contacts (IRR = 1.09, CI: 1.01-1.18) and fewer contacts were reported on weekends (IRR = 0.95, CI: 0.90-0.99). School had the highest proportion of contact durations exceeding 4 h (49.5%) and daily frequencies (90.4%), followed by home and workplace. The contact patterns exhibited clear age-assortative mixing, with Q indices of 0.27 and 0.28.

CONCLUSIONS

We assessed the characteristics of social contact patterns in Suzhou, which are essential for parameterizing models of infectious disease transmission. The high frequency and intensity of contacts among school-aged children should be given special attention, making school intervention policies a crucial component in controlling infectious disease transmission.

Evaluating Seasonal Variations in Human Contact Patterns and Their Impact on the Transmission of Respiratory Infectious Diseases

BACKGROUND

Human contact patterns are a key determinant driving the spread of respiratory infectious diseases. However, the relationship between contact patterns and seasonality as well as their possible association with the seasonality of respiratory diseases is yet to be clarified.

METHODS

We investigated the association between temperature and human contact patterns using data collected through a cross-sectional diary-based contact survey in Shanghai, China, between December 24, 2017, and May 30, 2018. We then developed a compartmental model of influenza transmission informed by the derived seasonal trends in the number of contacts and validated it against A(H1N1)pdm09 influenza data collected in Shanghai during the same period.

RESULTS

We identified a significant inverse relationship between the number of contacts and the seasonal temperature trend defined as a spline interpolation of temperature data (p = 0.003). We estimated an average of 16.4 (95% PrI: 15.1-17.5) contacts per day in December 2017 that increased to an average of 17.6 contacts (95% PrI: 16.5-19.3) in January 2018 and then declined to an average of 10.3 (95% PrI: 9.4-10.8) in May 2018. Estimates of influenza incidence obtained by the compartmental model comply with the observed epidemiological data. The reproduction number was estimated to increase from 1.24 (95% CI: 1.21-1.27) in December to a peak of 1.34 (95% CI: 1.31-1.37) in January. The estimated median infection attack rate at the end of the season was 27.4% (95% CI: 23.7-30.5%).

CONCLUSIONS

Our findings support a relationship between temperature and contact patterns, which can contribute to deepen the understanding of the relationship between social interactions and the epidemiology of respiratory infectious diseases.

Is income inequality linked to infectious disease prevalence? A hypothesis-generating study using tuberculosis

We study the association between infectious disease incidence and income inequality. We hypothesize that random social mixing in an income-unequal society brings into contact a) susceptible and infected poor and b) the infected-poor and the susceptible-rich, raising infectious disease incidence. We analyzed publicly available, country-level panel data for a large cross-section of countries between 1995 and 2013 to examine whether countries with elevated levels of income inequality have higher rates of pulmonary Tuberculosis (TB) incidence per capita. A "negative control" using anemia and diabetes (both non-communicable diseases and hence impervious to the hypothesized mechanism) is also applied. We find that high levels of income inequality are positively associated with tuberculosis incidence. All else equal, countries with income-Gini coefficients 10% apart show a statistically significant 4% difference in tuberculosis incidence. Income inequality had a null effect on the negative controls. Our cross-country regression results suggest that income inequality may create conditions where TB spreads more easily, and policy action to reduce income inequities could directly contribute to a reduced TB burden.

Smoking, dietary factors and major age-related eye disorders: an umbrella review of systematic reviews and meta-analyses

BACKGROUND

There is accumulating evidence of the associations between age-related eye diseases (AREDs) and smoking or dietary factors. We aimed to provide an umbrella review of the published literature pertaining to smoking or dietary intake as risk factors for major AREDs including cataract, glaucoma, age-related macular degeneration (AMD) and diabetic retinopathy.

METHODS

We searched for pertinent systematic reviews or meta-analyses in PubMed and Web of Science until 16 April 2022. We reperformed the meta-analysis of each association using random effects models. The heterogeneity and 95% prediction interval were calculated. The presence of small-study effect or excess significance bias was also assessed.

RESULTS

In total, 64 associations from 25 meta-analyses and 41 associations from 10 qualitative systematic reviews were evaluated. There was convincing (class I) evidence for only one association, namely current smoking and cataract. Two factors had highly suggestive (class II) evidence, namely ever smoking associated with cataract and fish consumption associated with AMD. We also found suggestive (class III) evidence for associations between the dietary intake of omega-3 polyunsaturated fatty acid, lutein, zeaxanthin, vitamin C and the risk of cataract.

CONCLUSIONS

Smoking as a risk factor for cataract was the most robust association we identified. We also identified several dietary elements associated with AREDs. Large prospective studies are warranted to further examine the associations discussed in this review.

PROSPERO REGISTRATION NUMBER

CRD42022339082.

Comprehensive profiling of social mixing patterns in resource poor countries: a mixed methods research protocol

Background

Low-and-middle-income countries (LMICs) bear a disproportionate burden of communicable diseases. Social interaction data inform infectious disease models and disease prevention strategies. The variations in demographics and contact patterns across ages, cultures, and locations significantly impact infectious disease dynamics and pathogen transmission. LMICs lack sufficient social interaction data for infectious disease modeling.

Methods

To address this gap, we will collect qualitative and quantitative data from eight study sites (encompassing both rural and urban settings) across Guatemala, India, Pakistan, and Mozambique. We will conduct focus group discussions and cognitive interviews to assess the feasibility and acceptability of our data collection tools at each site. Thematic and rapid analyses will help to identify key themes and categories through coding, guiding the design of quantitative data collection tools (enrollment survey, contact diaries, exit survey, and wearable proximity sensors) and the implementation of study procedures.We will create three age-specific contact matrices (physical, nonphysical, and both) at each study site using data from standardized contact diaries to characterize the patterns of social mixing. Regression analysis will be conducted to identify key drivers of contacts. We will comprehensively profile the frequency, duration, and intensity of infants' interactions with household members using high resolution data from the proximity sensors and calculating infants' proximity score (fraction of time spent by each household member in proximity with the infant, over the total infant contact time) for each household member.

Discussion

Our qualitative data yielded insights into the perceptions and acceptability of contact diaries and wearable proximity sensors for collecting social mixing data in LMICs. The quantitative data will allow a more accurate representation of human interactions that lead to the transmission of pathogens through close contact in LMICs. Our findings will provide more appropriate social mixing data for parameterizing mathematical models of LMIC populations. Our study tools could be adapted for other studies.

Scale-free dynamics of COVID-19 in a Brazilian city

A common basis to address the dynamics of directly transmitted infectious diseases, such as COVID-19, are compartmental (or SIR) models. SIR models typically assume homogenous population mixing, a simplification that is convenient but unrealistic. Here we validate an existing model of a scale-free fractal infection process using high-resolution data on COVID-19 spread in São Caetano, Brazil. We find that transmission can be described by a network in which each infectious individual has a small number of susceptible contacts, of the order of 2-5. This model parameter correlated tightly with physical distancing measured by mobile phone data, such that in periods of greater distancing the model recovered a lower average number of contacts, and vice versa. We show that the SIR model is a special case of our scale-free fractal process model in which the parameter that reflects population structure is set at unity, indicating homogeneous mixing. Our more general framework better explained the dynamics of COVID-19 in São Caetano, used fewer parameters than a standard SIR model and accounted for geographically localized clusters of disease. Our model requires further validation in other locations and with other directly transmitted infectious agents.

Quantifying social contact patterns in Minnesota during stay-at-home social distancing order

SARS-CoV-2 is primarily transmitted through person-to-person contacts. It is important to collect information on age-specific contact patterns because SARS-CoV-2 susceptibility, transmission, and morbidity vary by age. To reduce the risk of infection, social distancing measures have been implemented. Social contact data, which identify who has contact with whom especially by age and place are needed to identify high-risk groups and serve to inform the design of non-pharmaceutical interventions. We estimated and used negative binomial regression to compare the number of daily contacts during the first round (April-May 2020) of the Minnesota Social Contact Study, based on respondent's age, gender, race/ethnicity, region, and other demographic characteristics. We used information on the age and location of contacts to generate age-structured contact matrices. Finally, we compared the age-structured contact matrices during the stay-at-home order to pre-pandemic matrices. During the state-wide stay-home order, the mean daily number of contacts was 5.7. We found significant variation in contacts by age, gender, race, and region. Adults between 40 and 50 years had the highest number of contacts. The way race/ethnicity was coded influenced patterns between groups. Respondents living in Black households (which includes many White respondents living in inter-racial households with black family members) had 2.7 more contacts than respondents in White households; we did not find this same pattern when we focused on individual's reported race/ethnicity. Asian or Pacific Islander respondents or in API households had approximately the same number of contacts as respondents in White households. Respondents in Hispanic households had approximately two fewer contacts compared to White households, likewise Hispanic respondents had three fewer contacts than White respondents. Most contacts were with other individuals in the same age group. Compared to the pre-pandemic period, the biggest declines occurred in contacts between children, and contacts between those over 60 with those below 60.

Social contact patterns relevant for infectious disease transmission in Cambodia

Social mixing patterns are key determinants of infectious disease transmission. Mathematical models parameterised with empirical data from contact pattern surveys have played an important role in understanding epidemic dynamics and informing control strategies, including for SARS-CoV-2. However, there is a paucity of data on social mixing patterns in many settings. We conducted a community-based survey in Cambodia in 2012 to characterise mixing patterns and generate setting-specific contact matrices according to age and urban/rural populations. Data were collected using a diary-based approach from 2016 participants, selected by stratified random sampling. Contact patterns were highly age-assortative, with clear intergenerational mixing between household members. Both home and school were high-intensity contact settings, with 27.7% of contacts occurring at home with non-household members. Social mixing patterns differed between rural and urban residents; rural participants tended to have more intergenerational mixing, and a higher number of contacts outside of home, work or school. Participants had low spatial mobility, with 88% of contacts occurring within 1 km of the participants' homes. These data broaden the evidence-base on social mixing patterns in low and middle-income countries and Southeast Asia, and highlight within-country heterogeneities which may be important to consider when modelling the dynamics of pathogens transmitted via close contact.

Determining meteorologically-favorable zones for seasonal influenza activity in Hong Kong

Investigations of simple and accurate meteorology classification systems for influenza epidemics, particularly in subtropical regions, are limited. To assist in preparing for potential upsurges in the demand on healthcare facilities during influenza seasons, our study aims to develop a set of meteorologically-favorable zones for epidemics of influenza A and B, defined as the intervals of meteorological variables with prediction performance optimized. We collected weekly detection rates of laboratory-confirmed influenza cases from four local major hospitals in Hong Kong between 2004 and 2019. Meteorological and air quality records for hospitals were collected from their closest monitoring stations. We employed classification and regression trees to identify zones that optimize the prediction performance of meteorological data in influenza epidemics, defined as a weekly rate > 50^th percentile over a year. According to the results, a combination of temperature > 25.1℃ and relative humidity > 79% was favorable to epidemics in hot seasons, whereas either temperature < 16.4℃ or a combination of < 20.4℃ and relative humidity > 76% was favorable to epidemics in cold seasons. The area under the receiver operating characteristic curve (AUC) in model training achieved 0.80 (95% confidence interval [CI], 0.76-0.83) and was kept at 0.71 (95%CI, 0.65-0.77) in validation. The meteorologically-favorable zones for predicting influenza A or A and B epidemics together were similar, but the AUC for predicting influenza B epidemics was comparatively lower. In conclusion, we established meteorologically-favorable zones for influenza A and B epidemics with a satisfactory prediction performance, even though the influenza seasonality in this subtropical setting was weak and type-specific.

Modeling and Global Sensitivity Analysis of Strategies to Mitigate Covid-19 Transmission on a Structured College Campus

In response to the COVID-19 pandemic, many higher educational institutions moved their courses on-line in hopes of slowing disease spread. The advent of multiple highly-effective vaccines offers the promise of a return to "normal" in-person operations, but it is not clear if-or for how long-campuses should employ non-pharmaceutical interventions such as requiring masks or capping the size of in-person courses. In this study, we develop and fine-tune a model of COVID-19 spread to UC Merced's student and faculty population. We perform a global sensitivity analysis to consider how both pharmaceutical and non-pharmaceutical interventions impact disease spread. Our work reveals that vaccines alone may not be sufficient to eradicate disease dynamics and that significant contact with an infectious surrounding community will maintain infections on-campus. Our work provides a foundation for higher-education planning allowing campuses to balance the benefits of in-person instruction with the ability to quarantine/isolate infectious individuals.

Mathematical Modeling of COVID-19 Transmission and Intervention in South Korea: A Review of Literature

South Korea implemented interventions to curb the spread of the novel coronavirus disease 2019 (COVID-19) pandemic with discovery of the first case in early 2020. Mathematical modeling designed to reflect the dynamics of disease transmission has been shown to be an important tool for responding to COVID-19. This study aimed to review publications on the structure, method, and role of mathematical models focusing on COVID-19 transmission dynamics in Korea. In total, 42 papers published between August 7, 2020 and August 21, 2022 were studied and reviewed. This study highlights the construction and utilization of mathematical models to help craft strategies for predicting the course of an epidemic and evaluating the effectiveness of control strategies. Despite the limitations caused by a lack of available epidemiological and surveillance data, modeling studies could contribute to providing scientific evidence for policymaking by simulating various scenarios.

Social contacts and other risk factors for respiratory infections among internally displaced people in Somaliland

BACKGROUND

Populations affected by humanitarian crises experience high burdens of acute respiratory infections (ARI), potentially driven by risk factors for severe disease such as poor nutrition and underlying conditions, and risk factors that may increase transmission such as overcrowding and the possibility of high social mixing. However, little is known about social mixing patterns in these populations.

METHODS

We conducted a cross-sectional social contact survey among internally displaced people (IDP) living in Digaale, a permanent IDP camp in Somaliland. We included questions on household demographics, shelter quality, crowding, travel frequency, health status, and recent diagnosis of pneumonia, and assessed anthropometric status in children. We present the prevalence of several risk factors relevant to transmission of respiratory infections, and calculated age-standardised social contact matrices to assess population mixing.

RESULTS

We found crowded households with high proportions of recent self-reported pneumonia (46% in children). 20% of children younger than five are stunted, and crude death rates are high in all age groups. ARI risk factors were common. Participants reported around 10 direct contacts per day. Social contact patterns are assortative by age, and physical contact rates are very high (78%).

CONCLUSIONS

ARI risk factors are very common in this population, while the large degree of contacts that involve physical touch could further increase transmission. Such IDP settings potentially present a perfect storm of risk factors for ARIs and their transmission, and innovative approaches to address such risks are urgently needed.

Virus spread on a scale-free network reproduces the Gompertz growth observed in isolated COVID-19 outbreaks

The counts of confirmed cases and deaths in isolated SARS-CoV-2 outbreaks follow the Gompertz growth function for locations of very different sizes. This lack of dependence on region size leads us to hypothesize that virus spread depends on the universal properties of the network of social interactions. We test this hypothesis by simulating the propagation of a virus on networks of different topologies or connectivities. Our main finding is that we can reproduce the Gompertz growth observed for many early outbreaks with a simple virus spread model on a scale-free network, in which nodes with many more neighbors than average are common. Nodes that have very many neighbors are infected early in the outbreak and then spread the infection very rapidly. When these nodes are no longer infectious, the remaining nodes that have most neighbors take over and continue to spread the infection. In this way, the rate of spread is fastest at the very start and slows down immediately. Geometrically we see that the "surface" of the epidemic, the number of susceptible nodes in contact with the infected nodes, starts to rapidly decrease very early in the epidemic and as soon as the larger nodes have been infected. In our simulation, the speed and impact of an outbreak depend on three parameters: the average number of contacts each node makes, the probability of being infected by a neighbor, and the probability of recovery. Intelligent interventions to reduce the impact of future outbreaks need to focus on these critical parameters in order to minimize economic and social collateral damage.

A mixed methods analysis of participation in a social contact survey

BACKGROUND

Social contact survey data forms a core component of modern epidemic models: however, there has been little assessment of the potential biases in such data.

METHODS

We conducted focus groups with university students who had (n = 13) and had never (n = 14) completed a social contact survey during the COVID-19 pandemic. Qualitative findings were explored quantitatively by analysing participation data.

RESULTS

The opportunity to contribute to COVID-19 research, to be heard and feel useful were frequently reported motivators for participating in the contact survey. Reductions in survey engagement following lifting of COVID-19 restrictions may have occurred because the research was perceived to be less critical and/or because the participants were busier and had more contacts. Having a high number of contacts to report, uncertainty around how to report each contact, and concerns around confidentiality were identified as factors leading to inaccurate reporting. Focus groups participants thought that financial incentives or provision of study results would encourage participation.

CONCLUSIONS

Incentives could improve engagement with social contact surveys. Qualitative research can inform the format, timing, and wording of surveys to optimise completion and accuracy.

Assessing the spatial variability of raising public risk awareness for the intervention performance of COVID-19 voluntary screening: A spatial simulation approach

The rapid spread of a (re)emerging pandemic (e.g., COVID-19) is usually attributed to the invisible transmission caused by asymptomatic cases. Health authorities rely on large-scale voluntary screening to identify and isolate invisible spreaders as well as symptomatic people as early as possible to control disease spread. Raising public awareness is beneficial for improving the effectiveness of epidemic prevention because it could increase the usage and demand for testing kits. However, the effectiveness of testing could be influenced by the spatial demand for medical resources in different periods. Spatial demand could also be triggered by public awareness in areas with two geographical factors, including spatial proximity to resources and attractiveness of human mobility. Therefore, it is necessary to explore the spatial variations in raising public awareness on the effectiveness of COVID-19 screening. We implemented spatial simulation models to integrate various levels of public awareness and pandemic dynamics in time and space. Moreover, we also assessed the effects of the spatial proximity of testing kits and the ease of human mobility on COVID-19 testing at various levels of public awareness. Our results indicated that high public awareness promotes high willingness to be tested. This causes the demand to not be fully satisfied at the peak times during a pandemic, yet the shortage of tests does not significantly increase pandemic severity. We also found that when public awareness is low, concentrating on unattractive areas (such as residential or urban fringe areas) could promote a higher benefit of testing. On the other hand, when awareness is high, the factor of distances to testing stations is more important for promoting the benefit of testing; allocating additional testing resources in areas distant from stations could have a higher benefit of testing. This study aims to provide insights for health authorities into the allocation of testing resources against disease outbreaks with respect to various levels of public awareness.

Cost-Benefit Analysis of Interventions to Mitigate the Monkeypox Virus

When a viral outbreak occurs, governments are obligated to protect their citizens from the diverse adverse effects of the disease. Health policymakers often have several interventions to consider based on the health of the population, as well as the cascading social and economic consequences of the possible mitigation strategies. The current outbreak of the monkeypox virus has elicited debate on the best mitigation strategy, especially given that most world economies are still recovering from the harsh economic effects of the COVID-19 pandemic. This paper sought to analyze the costs and benefits of three possible strategies and determine which option has the best health outcomes and positive economic effects. A case study of Jeddah was performed, whereby a model was simulated to determine the number of infections over 28 days based on one case of the monkeypox virus. Findings reveal that the vaccination provides the best intervention, as it effectively reduces the transmission rate and prevents loss of lives in the city. From the model, only three people were infected over the research period, while no deaths were reported. Although vaccination incurs a huge direct cost at the beginning, in the long run, it saves the economy from the disease's financial burden in terms of productivity loss from work absenteeism and premature deaths.

Health behavior homophily can mitigate the spread of infectious diseases in small-world networks

Research has repeatedly shown that the spread of infectious diseases is influenced by properties of our social networks. Small-world like structures with densely connected clusters bridged by only a few connections, for example, are not only known to diminish disease spread, but also to increase the chance for a disease to spread to any part of the network. Clusters composed of individuals who show similar reactions to avoid infections (health behavior homophily), however, might change the effect of such clusters on disease spread. To study the combined effect of health behavior homophily and small-world network properties on disease spread, we extend a previously developed ego-centered network formation model and agent-based simulation. Based on more than 80,000 simulated epidemics on generated networks varying in clustering and homophily, as well as diseases varying in severity and infectivity, we predict that the existence of health behavior homophilous clusters reduce the number of infections, lower peak size, and flatten the curve of active cases. That is because agents perceiving higher risks of infections can protect their cluster from infections comparatively quickly by severing only a few bridging ties. A comparison with epidemics in static network structures shows that the incapability to act upon risk perceptions and the low connectivity between clusters in static networks lead to diametrically opposed effects with comparatively large epidemics and prolonged epidemics. These finding suggest that micro-level behavioral adaptation to health risks mitigate macro-level disease spread to an extent that is not captured by static network models of disease spread. Furthermore, this mechanism can be used to design information campaigns targeting proxies for groups with lower risk perception.

Micro-level social structures and the success of COVID-19 national policies

Similar policies in response to the COVID-19 pandemic have resulted in different success rates. Although many factors are responsible for the variances in policy success, our study shows that the micro-level structure of person-to-person interactions-measured by the average household size and in-person social contact rate-can be an important explanatory factor. To create an explainable model, we propose a network transformation algorithm to create a simple and computationally efficient scaled network based on these micro-level parameters, as well as incorporate national-level policy data in the network dynamic for SEIR simulations. The model was validated during the early stages of the COVID-19 pandemic, which demonstrated that it can reproduce the dynamic ordinal ranking and trend of infected cases of various European countries that are sufficiently similar in terms of some socio-cultural factors. We also performed several counterfactual analyses to illustrate how policy-based scenario analysis can be performed rapidly and easily with these explainable models.

Cohort profile: the British Columbia COVID-19 Population Mixing Patterns Survey (BC-Mix)

PURPOSE

Several non-pharmaceutical interventions, such as physical distancing, handwashing, self-isolation, and school and business closures, were implemented in British Columbia (BC) following the first laboratory-confirmed case of COVID-19 on 26 January 2020, to minimise in-person contacts that could spread infections. The BC COVID-19 Population Mixing Patterns Survey (BC-Mix) was established as a surveillance system to measure behaviour and contact patterns in BC over time to inform the timing of the easing/re-imposition of control measures. In this paper, we describe the BC-Mix survey design and the demographic characteristics of respondents.

PARTICIPANTS

The ongoing repeated online survey was launched in September 2020. Participants are mainly recruited through social media platforms (including Instagram, Facebook, YouTube, WhatsApp). A follow-up survey is sent to participants 2-4 weeks after completing the baseline survey. Survey responses are weighted to BC's population by age, sex, geography and ethnicity to obtain generalisable estimates. Additional indices such as the Material and Social Deprivation Index, residential instability, economic dependency, and others are generated using census and location data.

FINDINGS TO DATE

As of 26 July 2021, over 61 000 baseline survey responses were received of which 41 375 were eligible for analysis. Of the eligible participants, about 60% consented to follow-up and about 27% provided their personal health numbers for linkage with healthcare databases. Approximately 83.5% of respondents were female, 58.7% were 55 years or older, 87.5% identified as white and 45.9% had at least a university degree. After weighting, approximately 50% were female, 39% were 55 years or older, 65% identified as white and 50% had at least a university degree.

FUTURE PLANS

Multiple papers describing contact patterns, physical distancing measures, regular handwashing and facemask wearing, modelling looking at impact of physical distancing measures and vaccine acceptance, hesitancy and uptake are either in progress or have been published.

A generalized epidemiological model with dynamic and asymptomatic population

In this paper, we develop an extension of compartmental epidemiological models which is suitable for COVID-19. The model presented in this paper comprises seven compartments in the progression of the disease. This model, named as the SINTRUE (Susceptible, Infected and pre-symptomatic, Infected and Symptomatic but Not Tested, Tested Positive, Recorded Recovered, Unrecorded Recovered, and Expired) model. The proposed model incorporates transmission due to asymptomatic carriers and captures the spread of the disease due to the movement of people to/from different administrative boundaries within a country. In addition, the model allows estimating the number of undocumented infections in the population and the number of unrecorded recoveries. The associated parameters in the model can help architect the public health policy and operational management of the pandemic. The results show that the testing rate of the asymptomatic patients is a crucial parameter to fight against the pandemic. The model is also shown to have a better predictive capability than the other epidemiological models.

Use of Contact Networks to Estimate Potential Pathogen Risk Exposure in Hospitals

IMPORTANCE

Person-to-person contact is important for the transmission of health care-associated pathogens. Quantifying these contact patterns is crucial for modeling disease transmission and understanding routes of potential transmission.

OBJECTIVE

To generate and analyze the mixing matrices of hospital patients based on their contacts within hospital units.

DESIGN, SETTING, AND PARTICIPANTS

In this quality improvement study, mixing matrices were created using a weighted contact network of connected hospital patients, in which contact was defined as occupying the same hospital unit for 1 day. Participants included hospitalized patients at 299 hospital units in 24 hospitals in the Southeastern United States that were part of the Duke Antimicrobial Stewardship Outreach Network between January 2015 and December 2017. Analysis was conducted between October 2021 and February 2022.

MAIN OUTCOMES AND MEASURES

The mixing matrices of patients for each hospital unit were assessed using age, Elixhauser Score, and a measure of antibiotic exposure.

RESULTS

Among 1 549 413 hospitalized patients (median [IQR] age, 44 [26-63] years; 883 580 [56.3%] women) in 299 hospital units, some units had highly similar patterns across multiple hospitals, although the number of patients varied to a great extent. For most of the adult inpatient units, frequent mixing was observed for older adult groups, while outpatient units (eg, emergency departments and behavioral health units) showed mixing between different age groups. Most units mixing patterns followed the marginal distribution of age; however, patients aged 90 years or older with longer lengths of stay created a secondary peak in some medical wards. From the mixing matrices by Elixhauser Score, mixing between patients with relatively higher comorbidity index was observed in intensive care units. Mixing matrices by antibiotic spectrum, a 4-point scale based on priority for antibiotic stewardship programs, resulted in 6 major distinct patterns owing to the variation of the type of antibiotics used in different units, namely those dominated by a single antibiotic spectrum (narrow, broad, or extended), 1 pattern spanning all antibiotic spectrum types and 2 forms of narrow- and extended-spectrum dominant exposure patterns (an emergency room where patients were exposed to one type of antibiotic or the other and a pediatric ward where patients were exposed to both types).

CONCLUSIONS AND RELEVANCE

This quality improvement study found that the mixing patterns of patients both within and between hospitals followed broadly expected patterns, although with a considerable amount of heterogeneity. These patterns could be used to inform mathematical models of health care-associated infections, assess the appropriateness of both models and policies for smaller community hospitals, and provide baseline information for the design of interventions that rely on altering patient contact patterns, such as practices for transferring patients within hospitals.

Patterns of human social contact and mask wearing in high-risk groups in China

Background

The pandemic of coronavirus disease 2019 (COVID-19) has changed human behavior in areas such as contact patterns and mask-wearing frequency. Exploring human–human contact patterns and mask-wearing habits in high-risk groups is an essential step in fully understanding the transmission of respiratory infection-based diseases. This study had aims to quantify local human–human (H–H) contacts in high-risk groups in representative provinces of China and to explore the occupation-specific assortativity and heterogeneity of social contacts.

Methods

Delivery workers, medical workers, preschoolers, and students from Qinghai, Shanghai, and Zhejiang were recruited to complete an online questionnaire that queried general information, logged contacts, and assessed the willingness to wear a mask in different settings. The “group contact” was defined as contact with a group at least 20 individuals. The numbers of contacts across different characteristics were assessed and age-specific contact matrices were established. A generalized additive mixed model was used to analyze the associations between the number of individual contacts and several characteristics. The factors influencing the frequency of mask wearing were evaluated with a logistic regression model.

Results

A total of 611,287 contacts were reported by 15,635 participants. The frequency of daily individual contacts averaged 3.14 (95% confidence interval: 3.13–3.15) people per day, while that of group contacts was 37.90 (95% CI: 37.20–38.70). Skin-to-skin contact and long-duration contact were more likely to occur at home or among family members. Contact matrices of students were the most assortative (all contacts q-index = 0.899, 95% CI: 0.894–0.904). Participants with larger household sizes reported having more contacts. Higher household income per capita was significantly associated with a greater number of contacts among preschoolers (P_{50,000–99,999} = 0.033) and students (P_{10,000–29,999} = 0.017). In each of the public places, the frequency of mask wearing was highest for delivery workers. For preschoolers and students with more contacts, the proportion of those who reported always wearing masks was lower (P < 0.05) in schools/workplaces and public transportation than preschoolers and students with fewer contacts.

Conclusions

Contact screening efforts should be concentrated in the home, school, and workplace after an outbreak of an epidemic, as more than 75% of all contacts, on average, will be found in such places. Efforts should be made to improve the mask-wearing rate and age-specific health promotion measures aimed at reducing transmission for the younger demographic. Age-stratified and occupation-specific social contact research in high-risk groups could help inform policy-making decisions during the post-relaxation period of the COVID-19 pandemic.

Graphical Abstract

Supplementary Information

The online version contains supplementary material available at 10.1186/s40249-022-00988-8.

Individual variation in susceptibility or exposure to SARS-CoV-2 lowers the herd immunity threshold

Individual variation in susceptibility and exposure is subject to selection by natural infection, accelerating the acquisition of immunity, and reducing herd immunity thresholds and epidemic final sizes. This is a manifestation of a wider population phenomenon known as "frailty variation". Despite theoretical understanding, public health policies continue to be guided by mathematical models that leave out considerable variation and as a result inflate projected disease burdens and overestimate the impact of interventions. Here we focus on trajectories of the coronavirus disease (COVID-19) pandemic in England and Scotland until November 2021. We fit models to series of daily deaths and infer relevant epidemiological parameters, including coefficients of variation and effects of non-pharmaceutical interventions which we find in agreement with independent empirical estimates based on contact surveys. Our estimates are robust to whether the analysed data series encompass one or two pandemic waves and enable projections compatible with subsequent dynamics. We conclude that vaccination programmes may have contributed modestly to the acquisition of herd immunity in populations with high levels of pre-existing naturally acquired immunity, while being crucial to protect vulnerable individuals from severe outcomes as the virus becomes endemic.

Social Contacts and Transmission of COVID-19 in British Columbia, Canada

Background

Close-contact rates are thought to be a driving force behind the transmission of many infectious respiratory diseases. Yet, contact rates and their relation to transmission and the impact of control measures, are seldom quantified. We quantify the response of contact rates, reported cases and transmission of COVID-19, to public health contact-restriction orders, and examine the associations among these three variables in the province of British Columbia, Canada.

Methods

We derived time series data for contact rates, daily cases and transmission of COVID-19 from a social contacts survey, reported case counts and by fitting a transmission model to reported cases, respectively. We used segmented regression to investigate impacts of public health orders; Pearson correlation to determine associations between contact rates and transmission; and vector autoregressive modeling to quantify lagged associations between contacts rates, daily cases, and transmission.

Results

Declines in contact rates and transmission occurred concurrently with the announcement of public health orders, whereas declines in cases showed a reporting delay of about 2 weeks. Contact rates were a significant driver of COVID-19 and explained roughly 19 and 20% of the variation in new cases and transmission, respectively. Interestingly, increases in COVID-19 transmission and cases were followed by reduced contact rates: overall, daily cases explained about 10% of the variation in subsequent contact rates.

Conclusion

We showed that close-contact rates were a significant time-series driver of transmission and ultimately of reported cases of COVID-19 in British Columbia, Canada and that they varied in response to public health orders. Our results also suggest possible behavioral feedback, by which increased reported cases lead to reduced subsequent contact rates. Our findings help to explain and validate the commonly assumed, but rarely measured, response of close contact rates to public health guidelines and their impact on the dynamics of infectious diseases.

Determining Existing Human Population Immunity as Part of Assessing Influenza Pandemic Risk

Zoonotic influenza infections continue to threaten human health. Ongoing surveillance and risk assessment of animal viruses are needed for pandemic preparedness, and population immunity is an important component of risk assessment. We determined age-stratified hemagglutinin inhibition seroprevalence against 5 swine influenza viruses circulating in Hong Kong and Guangzhou in China. Using hemagglutinin inhibition seroprevalence and titers, we modeled the effect of population immunity on the basic reproduction number (R₀) if each virus were to become transmissible among humans. Among 353 individual serum samples, we reported low seroprevalence for triple-reassortant H1N2 and Eurasian avian-like H1N1 influenza viruses, which would reduce R₀ by only 18%–20%. The smallest R₀ needed to cause a pandemic was 1.22–1.24, meaning existing population immunity would be insufficient to block the spread of these H1N1 or H1N2 variants. For human-origin H3N2, existing population immunity could suppress R₀ by 47%, thus reducing pandemic risk.

Increased close proximity airborne transmission of the SARS-CoV-2 Delta variant

The Delta variant of SARS-CoV-2 causes higher viral loads in infected hosts, increasing the risk of close proximity airborne transmission through breathing, speaking and coughing. We performed a Monte Carlo simulation using a social contact network and exponential dose-response model to quantify the close proximity reproduction number of both wild-type SARS-CoV-2 and the Delta variant. We estimate more than twice as many Delta variant cases will reproduce infection in their close proximity contacts (64%) versus the wild-type SARS-CoV-2 (29%). Occupational health guidelines must consider close proximity airborne transmission and recommend improved personal respiratory protection for high-risk workers.

Reconstructing social mixing patterns via weighted contact matrices from online and representative surveys

The unprecedented behavioural responses of societies have been evidently shaping the COVID-19 pandemic, yet it is a significant challenge to accurately monitor the continuously changing social mixing patterns in real-time. Contact matrices, usually stratified by age, summarise interaction motifs efficiently, but their collection relies on conventional representative survey techniques, which are expensive and slow to obtain. Here we report a data collection effort involving over [Formula: see text] of the Hungarian population to simultaneously record contact matrices through a longitudinal online and sequence of representative phone surveys. To correct non-representative biases characterising the online data, by using census data and the representative samples we develop a reconstruction method to provide a scalable, cheap, and flexible way to dynamically obtain closer-to-representative contact matrices. Our results demonstrate that although some conventional socio-demographic characters correlate significantly with the change of contact numbers, the strongest predictors can be collected only via surveys techniques and combined with census data for the best reconstruction performance. We demonstrate the potential of combined online-offline data collections to understand the changing behavioural responses determining the future evolution of the outbreak, and to inform epidemic models with crucial data.

Optimization of COVID-19 vaccination and the role of individuals with a high number of contacts: A model based approach

We report strong evidence of the importance of contact hubs (or superspreaders) in mitigating the current COVID-19 pandemic. Contact hubs have a much larger number of contacts than the average in the population, and play a key role on the effectiveness of vaccination strategies. By using an age-structures compartmental SEIAHRV (Susceptible, Exposed, Infected symptomatic, Asymptomatic, Hospitalized, Recovered, Vaccinated) model, calibrated from available demographic and COVID-19 incidence, and considering separately those individuals with a much greater number of contacts than the average in the population, we show that carefully choosing who will compose the first group to be vaccinated can impact positively the total death toll and the demand for health services. This is even more relevant in countries with a lack of basic resources for proper vaccination and a significant reduction in social isolation. In order to demonstrate our approach we show the effect of hypothetical vaccination scenarios in two countries of very different scales and mitigation policies, Brazil and Portugal.

Estimating local outbreak risks and the effects of non-pharmaceutical interventions in age-structured populations: SARS-CoV-2 as a case study

During the COVID-19 pandemic, non-pharmaceutical interventions (NPIs) including school closures, workplace closures and social distancing policies have been employed worldwide to reduce transmission and prevent local outbreaks. However, transmission and the effectiveness of NPIs depend strongly on age-related factors including heterogeneities in contact patterns and pathophysiology. Here, using SARS-CoV-2 as a case study, we develop a branching process model for assessing the risk that an infectious case arriving in a new location will initiate a local outbreak, accounting for the age distribution of the host population. We show that the risk of a local outbreak depends on the age of the index case, and we explore the effects of NPIs targeting individuals of different ages. Social distancing policies that reduce contacts outside of schools and workplaces and target individuals of all ages are predicted to reduce local outbreak risks substantially, whereas school closures have a more limited impact. In the scenarios considered here, when different NPIs are used in combination the risk of local outbreaks can be eliminated. We also show that heightened surveillance of infectious individuals reduces the level of NPIs required to prevent local outbreaks, particularly if enhanced surveillance of symptomatic cases is combined with efforts to find and isolate nonsymptomatic infected individuals. Our results reflect real-world experience of the COVID-19 pandemic, during which combinations of intense NPIs have reduced transmission and the risk of local outbreaks. The general modelling framework that we present can be used to estimate local outbreak risks during future epidemics of a range of pathogens, accounting fully for age-related factors.

Effect of COVID-19 driven lockdown on social contact pattern in Puducherry, India: A longitudinal study

Objective

This study was undertaken to assess the change in social contact and transmission dynamics among adults in the Puducherry district during the different phases of country-wide lockdown.

Methods

Adults aged 18-69 years in Puducherry were assessed for frequency and duration of contacts in the following time points: prior to lockdown (March 2020), during lockdown, immediate post-lockdown (April, June 2020), and seven months post-lockdown (February 2021). Adjusted incidence rate ratios (aIRR) were obtained using a generalized estimating equation. We also assessed the exponential trajectory of the time-varying reproduction number (R_t) during and after lockdown.

Results

Compared to pre-lockdown phase, frequency of social contacts during 1^st week, 4^th week of lockdown, and immediate post-lockdown were reduced by 89% (aIRR = 0.11; 95% CI: 0.09-0.13), 40% (aIRR = 0.60; 95% CI: 0.52-0.69) and 91% (aIRR = 0.09; 95% CI: 0.07-0.10) respectively. However, the decline was not statistically significant at seven months post-lockdown. Correspondingly, we observed an initial spike in R_t during the lockdown phase followed by a gradual decline during the immediate post-lockdown phase. However, seven months post-lockdown, R_t has increased again.

Conclusion

The study showed high compliance to the lockdown measures in Puducherry during the lockdown and immediate post-lockdown periods. However, as the lockdown measures were relaxed, the contact rate returned to the pre-lockdown state.

Changing Disparities in Coronavirus Disease 2019 (COVID-19) Burden in the Ethnically Homogeneous Population of Hong Kong Through Pandemic Waves: An Observational Study

BACKGROUND

Disparities were marked in previous pandemics, usually with higher attack rates reported for those in lower socioeconomic positions and for ethnic minorities.

METHODS

We examined characteristics of laboratory-confirmed coronavirus disease 2019 (COVID-19) cases in Hong Kong, assessed associations between incidence and population-level characteristics at the level of small geographic areas, and evaluated relations between socioeconomics and work-from-home (WFH) arrangements.

RESULTS

The largest source of COVID-19 importations switched from students studying overseas in the second wave to foreign domestic helpers in the third. The local cases were mostly individuals not in formal employment (retirees and homemakers) and production workers who were unable to WFH. For every 10% increase in the proportion of population employed as executives or professionals in a given geographic region, there was an 84% (95% confidence interval [CI], 1-97%) reduction in the incidence of COVID-19 during the third wave. In contrast, in the first 2 waves, the same was associated with 3.69 times (95% CI, 1.02-13.33) higher incidence. Executives and professionals were more likely to implement WFH and experienced frequent changes in WFH practice compared with production workers.

CONCLUSIONS

Consistent findings on the reversed socioeconomic patterning of COVID-19 burden between infection waves in Hong Kong in both individual- and population-level analyses indicated that risks of infections may be related to occupations involving high exposure frequency and WFH flexibility. Contextual determinants should be taken into account in policy planning aiming at mitigating such disparities.

Extended SEIR Model for Health Policies Assessment Against the COVID-19 Pandemic: the Case of Argentina

This work presents an extended and age-band compartmentalised SEIR model that allows describing the spread evolution of SARS-CoV-2 and evaluating the effect of different detection rates, vaccination strategies or immunity periods. The model splits up the population into fifteen age groups of 5 years each, linked through a statistical interaction matrix that includes seventeen health states within each age group. An age-dependent transmission rate takes into account infectious between the groups as well the effect of interventions such as quarantines and mobility restrictions. Further, the proposal includes a nonlinear switched controller for model tuning purposes guarantying a simple and fast adjusting process. To illustrate the model potentials, the particular case of COVID-19 evolution in Argentina is analysed by simulation of three scenarios: (i) different detection levels combined with mobility restrictions, (ii) vaccination campaigns with re-opening of activities and (iii) vaccination campaigns with possible reinfections. The results exhibit how the model can aid the authorities in the decision making process.

Social contact patterns and implications for infectious disease transmission: a systematic review and meta-analysis of contact surveys

Background:

Transmission of respiratory pathogens such as SARS-CoV-2 depends on patterns of contact and mixing across populations. Understanding this is crucial to predict pathogen spread and the effectiveness of control efforts. Most analyses of contact patterns to date have focused on high-income settings.

Methods:

Here, we conduct a systematic review and individual-participant meta-analysis of surveys carried out in low- and middle-income countries and compare patterns of contact in these settings to surveys previously carried out in high-income countries. Using individual-level data from 28,503 participants and 413,069 contacts across 27 surveys, we explored how contact characteristics (number, location, duration, and whether physical) vary across income settings.

Results:

Contact rates declined with age in high- and upper-middle-income settings, but not in low-income settings, where adults aged 65+ made similar numbers of contacts as younger individuals and mixed with all age groups. Across all settings, increasing household size was a key determinant of contact frequency and characteristics, with low-income settings characterised by the largest, most intergenerational households. A higher proportion of contacts were made at home in low-income settings, and work/school contacts were more frequent in high-income strata. We also observed contrasting effects of gender across income strata on the frequency, duration, and type of contacts individuals made.

Conclusions:

These differences in contact patterns between settings have material consequences for both spread of respiratory pathogens and the effectiveness of different non-pharmaceutical interventions.

Funding:

This work is primarily being funded by joint Centre funding from the UK Medical Research Council and DFID (MR/R015600/1).

Infectious diseases, particularly those caused by airborne pathogens like SARS-CoV-2, spread by social contact, and understanding how people mix is critical in controlling outbreaks. To explore these patterns, researchers typically carry out large contact surveys. Participants are asked for personal information (such as gender, age and occupation), as well as details of recent social contacts, usually those that happened in the last 24 hours. This information includes, the age and gender of the contact, where the interaction happened, how long it lasted, and whether it involved physical touch.

These kinds of surveys help scientists to predict how infectious diseases might spread. But there is a problem: most of the data come from high-income countries, and there is evidence to suggest that social contact patterns differ between places. Therefore, data from these countries might not be useful for predicting how infections spread in lower-income regions.

Here, Mousa et al. have collected and combined data from 27 contact surveys carried out before the COVID-19 pandemic to see how baseline social interactions vary between high- and lower-income settings. The comparison revealed that, in higher-income countries, the number of daily contacts people made decreased with age. But, in lower-income countries, younger and older individuals made similar numbers of contacts and mixed with all age groups.

In higher-income countries, more contacts happened at work or school, while in low-income settings, more interactions happened at home and people were also more likely to live in larger, intergenerational households. Mousa et al. also found that gender affected how long contacts lasted and whether they involved physical contact, both of which are key risk factors for transmitting airborne pathogens.

These findings can help researchers to predict how infectious diseases might spread in different settings. They can also be used to assess how effective non-medical restrictions, like shielding of the elderly and workplace closures, will be at reducing transmissions in different parts of the world.

A Cross-Country Comparative Study on the Role of Information and Communication Technology Policy and Infrastructure to Curb the Spread of Novel Coronavirus

Background

Despite worldwide efforts, control of COVID-19 transmission and its after effects is lagging. As seen from the cases of SARS-CoV-2 and influenza, worldwide crises associated with infections and their side effects are likely to recur in the future because of extensive international interactions. Consequently, there is an urgent need to identify the factors that can mitigate disease spread. We observed that the transmission speed and severity of consequences of COVID-19 varied substantially across countries, signaling the need for a country-level investigation.

Objective

We aimed to investigate how distancing-enabling information and communications technology (ICT) infrastructure and medical ICT infrastructure, and related policies have affected the cumulative number of confirmed cases, fatality rate, and initial speed of transmission across different countries.

Methods

We analyzed the determinants of COVID-19 transmission during the relatively early days of the pandemic by conducting regression analysis based on our data for country-level characteristics, including demographics, culture, ICT infrastructure, policies, economic status, and transmission of COVID-19. To gain further insights, we conducted a subsample analysis for countries with low population density.

Results

Our full sample analysis showed that implied telehealth policy, which refers to the lack of a specific telehealth-related policy but presence of a general eHealth policy, was associated with lower fatality rates when controlled for cultural characteristics (P=.004). In particular, the fatality rate for countries with an implied telehealth policy was lower than that for others by 2.7%. Interestingly, stated telehealth policy, which refers to the existence of a specified telehealth policy, was found to not be associated with lower fatality rates (P=.30). Furthermore, countries with a government-run health website had 36% fewer confirmed cases than those without it, when controlled for cultural characteristics (P=.03). Our analysis further revealed that the interaction between implied telehealth policy and training ICT health was significant (P=.01), suggesting that implied telehealth policy may be more effective when in-service training on ICT is provided to health professionals. In addition, credit card ownership, as an enabler of convenient e-commerce transactions and distancing, showed a negative association with fatality rates in the full sample analysis (P=.04), but not in the subsample analysis (P=.76), highlighting that distancing-enabling ICT is more useful in densely populated countries.

Conclusions

Our findings demonstrate important relationships between national traits and COVID-19 infections, suggesting guidelines for policymakers to minimize the negative consequences of pandemics. The findings suggest physicians’ autonomous use of medical ICT and strategic allocation of distancing-enabling ICT infrastructure in countries with high population density to maximize efficiency. This study also encourages further research to investigate the role of health policies in combatting COVID-19 and other pandemics.

Individual's daily behaviour and intergenerational mixing in different social contexts of Kenya

We investigated contact patterns in diverse social contexts in Kenya and the daily behaviours that may play a pivotal role in infection transmission to the most vulnerable leveraging novel data from a 2-day survey on social contacts and time use (TU) from a sample of 1407 individuals (for a total of 2705 person days) from rural, urban formal, and informal settings. We used TU data to build six profiles of daily behaviour based on the main reported activities, i.e., Homestayers (71.1% of person days), Workers (9.3%), Schoolers (7.8%), or locations at increasing distance from home, i.e., Walkers (6.6%), Commuters (4.6%), Travelers (0.6%). In the rural setting, we observed higher daily contact numbers (11.56, SD 0.23) and percentages of intergenerational mixing with older adults (7.5% of contacts reported by those younger than 60 years vs. less than 4% in the urban settings). Overall, intergenerational mixing with older adults was higher for Walkers (7.3% of their reported contacts), Commuters (8.7%), and Homestayers (5.1%) than for Workers (1.5%) or Schoolers (3.6%). These results could be instrumental in defining effective interventions that acknowledge the heterogeneity in social contexts and daily routines, either in Kenya or other demographically and culturally similar sub-Saharan African settings.

Individual social contact data and population mobility data as early markers of SARS-CoV-2 transmission dynamics during the first wave in Germany-an analysis based on the COVIMOD study

BACKGROUND

The effect of contact reduction measures on infectious disease transmission can only be assessed indirectly and with considerable delay. However, individual social contact data and population mobility data can offer near real-time proxy information. The aim of this study is to compare social contact data and population mobility data with respect to their ability to reflect transmission dynamics during the first wave of the SARS-CoV-2 pandemic in Germany.

METHODS

We quantified the change in social contact patterns derived from self-reported contact survey data collected by the German COVIMOD study from 04/2020 to 06/2020 (compared to the pre-pandemic period from previous studies) and estimated the percentage mean reduction over time. We compared these results as well as the percentage mean reduction in population mobility data (corrected for pre-pandemic mobility) with and without the introduction of scaling factors and specific weights for different types of contacts and mobility to the relative reduction in transmission dynamics measured by changes in R values provided by the German Public Health Institute.

RESULTS

We observed the largest reduction in social contacts (90%, compared to pre-pandemic data) in late April corresponding to the strictest contact reduction measures. Thereafter, the reduction in contacts dropped continuously to a minimum of 73% in late June. Relative reduction of infection dynamics derived from contact survey data underestimated the one based on reported R values in the time of strictest contact reduction measures but reflected it well thereafter. Relative reduction of infection dynamics derived from mobility data overestimated the one based on reported R values considerably throughout the study. After the introduction of a scaling factor, specific weights for different types of contacts and mobility reduced the mean absolute percentage error considerably; in all analyses, estimates based on contact data reflected measured R values better than those based on mobility.

CONCLUSIONS

Contact survey data reflected infection dynamics better than population mobility data, indicating that both data sources cover different dimensions of infection dynamics. The use of contact type-specific weights reduced the mean absolute percentage errors to less than 1%. Measuring the changes in mobility alone is not sufficient for understanding the changes in transmission dynamics triggered by public health measures.

SOCRATES-CoMix: a platform for timely and open-source contact mixing data during and in between COVID-19 surges and interventions in over 20 European countries

BACKGROUND

SARS-CoV-2 dynamics are driven by human behaviour. Social contact data are of utmost importance in the context of transmission models of close-contact infections.

METHODS

Using online representative panels of adults reporting on their own behaviour as well as parents reporting on the behaviour of one of their children, we collect contact mixing (CoMix) behaviour in various phases of the COVID-19 pandemic in over 20 European countries. We provide these timely, repeated observations using an online platform: SOCRATES-CoMix. In addition to providing cleaned datasets to researchers, the platform allows users to extract contact matrices that can be stratified by age, type of day, intensity of the contact and gender. These observations provide insights on the relative impact of recommended or imposed social distance measures on contacts and can inform mathematical models on epidemic spread.

CONCLUSION

These data provide essential information for policymakers to balance non-pharmaceutical interventions, economic activity, mental health and wellbeing, during vaccine rollout.

Plateaus, rebounds and the effects of individual behaviours in epidemics

Plateaus and rebounds of various epidemiological indicators are widely reported in Covid-19 pandemics studies but have not been explained so far. Here, we address this problem and explain the appearance of these patterns. We start with an empirical study of an original dataset obtained from highly precise measurements of SARS-CoV-2 concentration in wastewater over nine months in several treatment plants around the Thau lagoon in France. Among various features, we observe that the concentration displays plateaus at different dates in various locations but at the same level. In order to understand these facts, we introduce a new mathematical model that takes into account the heterogeneity and the natural variability of individual behaviours. Our model shows that the distribution of risky behaviours appears as the key ingredient for understanding the observed temporal patterns of epidemics.

Projecting contact matrices in 177 geographical regions: An update and comparison with empirical data for the COVID-19 era

Mathematical models have played a key role in understanding the spread of directly-transmissible infectious diseases such as Coronavirus Disease 2019 (COVID-19), as well as the effectiveness of public health responses. As the risk of contracting directly-transmitted infections depends on who interacts with whom, mathematical models often use contact matrices to characterise the spread of infectious pathogens. These contact matrices are usually generated from diary-based contact surveys. However, the majority of places in the world do not have representative empirical contact studies, so synthetic contact matrices have been constructed using more widely available setting-specific survey data on household, school, classroom, and workplace composition combined with empirical data on contact patterns in Europe. In 2017, the largest set of synthetic contact matrices to date were published for 152 geographical locations. In this study, we update these matrices with the most recent data and extend our analysis to 177 geographical locations. Due to the observed geographic differences within countries, we also quantify contact patterns in rural and urban settings where data is available. Further, we compare both the 2017 and 2020 synthetic matrices to out-of-sample empirically-constructed contact matrices, and explore the effects of using both the empirical and synthetic contact matrices when modelling physical distancing interventions for the COVID-19 pandemic. We found that the synthetic contact matrices show qualitative similarities to the contact patterns in the empirically-constructed contact matrices. Models parameterised with the empirical and synthetic matrices generated similar findings with few differences observed in age groups where the empirical matrices have missing or aggregated age groups. This finding means that synthetic contact matrices may be used in modelling outbreaks in settings for which empirical studies have yet to be conducted.

Systematic Review and Meta-Analysis of Sex Differences in Social Contact Patterns and Implications for Tuberculosis Transmission and Control

Social contact patterns might contribute to excess burden of tuberculosis in men. We conducted a study of social contact surveys to evaluate contact patterns relevant to tuberculosis transmission. Available data describe 21 surveys in 17 countries and show profound differences in sex-based and age-based patterns of contact. Adults reported more adult contacts than children. Children preferentially mixed with women in all surveys (median sex assortativity 58%, interquartile range [IQR] 57%–59% for boys, 61% [IQR 60%–63%] for girls). Men and women reported sex-assortative mixing in 80% and 95% of surveys (median sex assortativity 56% [IQR 54%–58%] for men, 59% [IQR 57%–63%] for women). Sex-specific patterns of contact with adults were similar at home and outside the home for children; adults reported greater sex assortativity outside the home in most surveys. Sex assortativity in adult contacts likely contributes to sex disparities in adult tuberculosis burden by amplifying incidence among men.

Social Contact Patterns and Implications for Infectious Disease Transmission: A Systematic Review and Meta-Analysis of Contact Surveys

BACKGROUND

Transmission of respiratory pathogens such as SARS-CoV-2 depends on patterns of contact and mixing across populations. Understanding this is crucial to predict pathogen spread and the effectiveness of control efforts. Most analyses of contact patterns to date have focussed on high-income settings.

METHODS

Here, we conduct a systematic review and individual-participant meta-analysis of surveys carried out in low- and middle-income countries and compare patterns of contact in these settings to surveys previously carried out in high-income countries. Using individual-level data from 28,503 participants and 413,069 contacts across 27 surveys we explored how contact characteristics (number, location, duration and whether physical) vary across income settings.

RESULTS

Contact rates declined with age in high- and upper-middle-income settings, but not in low-income settings, where adults aged 65+ made similar numbers of contacts as younger individuals and mixed with all age-groups. Across all settings, increasing household size was a key determinant of contact frequency and characteristics, but low-income settings were characterised by the largest, most intergenerational households. A higher proportion of contacts were made at home in low-income settings, and work/school contacts were more frequent in high-income strata. We also observed contrasting effects of gender across income-strata on the frequency, duration and type of contacts individuals made.

CONCLUSIONS

These differences in contact patterns between settings have material consequences for both spread of respiratory pathogens, as well as the effectiveness of different non-pharmaceutical interventions.

FUNDING

This work is primarily being funded by joint Centre funding from the UK Medical Research Council and DFID (MR/R015600/1).

The evolution of the association between community level social capital and COVID-19 deaths and hospitalizations in the United States

We use county level data from the United States to document the role of social capital the evolution of COVID-19 between January 2020 and January 2021. We find that social capital differentials in COVID-19 deaths and hospitalizations depend on the dimension of social capital and the timeframe considered. Communities with higher levels of relational and cognitive social capital were especially successful in lowering COVID-19 deaths and hospitalizations than communities with lower social capital between late March and early April. A difference of one standard deviation in relational social capital corresponded to a reduction of 30% in the number of COVID-19 deaths recorded. After April 2020, differentials in COVID-19 deaths related to relational social capital persisted although they became progressively less pronounced. By contrast, the period of March-April 2020, our estimates suggest that there was no statistically significant difference in the number of deaths recorded in areas with different levels of cognitive social capital. In fact, from late June-early July onwards the number of new deaths recorded as being due to COVID-19 was higher in communities with higher levels of cognitive social capital. The overall number of deaths recorded between January 2020 and January 2021 was lower in communities with higher levels of relational social capital. Our findings suggest that the association between social capital and public health outcomes can vary greatly over time and across indicators of social capital.