Modelling disease mitigation at mass gatherings: A case study of COVID-19 at the 2022 FIFA World Cup.

The 2022 FIFA World Cup was the first major multi-continental sporting Mass Gathering Event (MGE) of the post COVID-19 era to allow foreign spectators. Such large-scale MGEs can potentially lead to outbreaks of infectious disease and contribute to the global dissemination of such pathogens. Here we adapt previous work and create a generalisable model framework for assessing the use of disease control strategies at such events, in terms of reducing infections and hospitalisations. This framework utilises a combination of meta-populations based on clusters of people and their vaccination status, Ordinary Differential Equation integration between fixed time events, and Latin Hypercube sampling. We use the FIFA 2022 World Cup as a case study for this framework (modelling each match as independent 7 day MGEs). Pre-travel screenings of visitors were found to have little effect in reducing COVID-19 infections and hospitalisations. With pre-match screenings of spectators and match staff being more effective. Rapid Antigen (RA) screenings 0.5 days before match day performed similarly to RT-PCR screenings 1.5 days before match day. Combinations of pre-travel and pre-match testing led to improvements. However, a policy of ensuring that all visitors had a COVID-19 vaccination (second or booster dose) within a few months before departure proved to be much more efficacious. The State of Qatar abandoned all COVID-19 related travel testing and vaccination requirements over the period of the World Cup. Our work suggests that the State of Qatar may have been correct in abandoning the pre-travel testing of visitors. However, there was a spike in COVID-19 cases and hospitalisations within Qatar over the World Cup. Given our findings and the spike in cases, we suggest a policy requiring visitors to have had a recent COVID-19 vaccination should have been in place to reduce cases and hospitalisations.

Dengue Endemicity, Force of Infection, and Variation in Transmission Intensity in 13 Endemic Countries.

Dengue endemicity varies but comparative, multicountry data are extremely limited. An improved understanding is needed to prioritize prevention, including vaccination, which is currently recommended only under specific epidemiological conditions. We used serological study data from 46 geographical sites in 13 countries to estimate dengue force of infection (FOI, the proportion of children seroconverting per year) under assumptions of either age-constant or age-varying FOI, and the age at which 50% and 80% of children had been infected. After exclusions, 13 661 subjects were included. Estimated constant FOI varied widely, from 1.7% (Singapore) to 24.1% (the Philippines). In the site-level analysis 44 sites (96%) reached 50% seroconversion and 35 sites (75%) reached 80% seroconversion by age 18 years, with significant heterogeneity. These findings confirm that children living in dengue-endemic countries receive intense early dengue exposure, increasing risk of secondary infection, and imply serosurveys at fine spatial resolutions are needed to inform vaccination campaigns.

Disease transmission and mass gatherings: a case study on meningococcal infection during Hajj.

BACKGROUND: Mass gatherings can not only trigger major outbreaks on-site but also facilitate global spread of infectious pathogens. Hajj is one of the largest mass gathering events worldwide where over two million pilgrims from all over the world gather annually creating intense congestion. METHODS: We developed a meta-population model to represent the transmission dynamics of Neisseria meningitidis and the impact of Hajj pilgrimage on the risk of invasive meningococcal disease (IMD) for pilgrims population, local population at the Hajj site and country of origin of Hajj pilgrims. This model was calibrated using data on IMD over 17 years (1995-2011) and further used to simulate potential changes in vaccine policy and endemic conditions. RESULTS: The effect of increased density of contacts during Hajj was estimated to generate a 78-fold increase in disease transmission that impacts not only pilgrims but also the local population. Quadrivalent ACWY vaccination was found to be very effective in reducing the risk of outbreak during Hajj. Hajj has more limited impact on IMD transmission and exportation in the pilgrim countries of origin, although not negligible given the size of the population considered. CONCLUSION: The analysis performed highlighted the amplifying effect of mass gathering on N. meningitidis transmission and confirm vaccination as a very effective preventive measure to mitigate outbreak risks.

Quantifying the annual incidence and underestimation of seasonal influenza: A modelling approach.

BACKGROUND: Seasonal influenza poses a significant public health and economic burden, associated with the outcome of infection and resulting complications. The true burden of the disease is difficult to capture due to the wide range of presentation, from asymptomatic cases to non-respiratory complications such as cardiovascular events, and its seasonal variability. An understanding of the magnitude of the true annual incidence of influenza is important to support prevention and control policy development and to evaluate the impact of preventative measures such as vaccination. METHODS: We use a dynamic disease transmission model, laboratory-confirmed influenza surveillance data, and randomized-controlled trial (RCT) data to quantify the underestimation factor, expansion factor, and symptomatic influenza illnesses in the US and Canada during the 2011-2012 and 2012-2013 influenza seasons. RESULTS: Based on 2 case definitions, we estimate between 0.42-3.2% and 0.33-1.2% of symptomatic influenza illnesses were laboratory-confirmed in Canada during the 2011-2012 and 2012-2013 seasons, respectively. In the US, we estimate between 0.08-0.61% and 0.07-0.33% of symptomatic influenza illnesses were laboratory-confirmed in the 2011-2012 and 2012-2013 seasons, respectively. We estimated the symptomatic influenza illnesses in Canada to be 0.32-2.4 million in 2011-2012 and 1.8-8.2 million in 2012-2013. In the US, we estimate the number of symptomatic influenza illnesses to be 4.4-34 million in 2011-2012 and 23-102 million in 2012-2013. CONCLUSIONS: We illustrate that monitoring a representative group within a population may aid in effectively modelling the transmission of infectious diseases such as influenza. In particular, the utilization of RCTs in models may enhance the accuracy of epidemiological parameter estimation.

REAL-WORLD EVIDENCE OF DENGUE BURDEN ON HOSPITALS IN MEXICO: INSIGHTS FROM THE AUTOMATED SUBSYSTEM OF HOSPITAL DISCHARGES (SAEH) DATABASE.

BACKGROUND: The incidence of dengue in Mexico has increased in recent decades. It has been suggested that dengue outbreaks may compromise treatment quality in hospitals. OBJECTIVE: The objective of the study was to quantify the burden imposed by dengue on hospital services in Mexico. METHODS: We analyzed 19.2 million records contained in the database of hospital services of the Mexican Ministry of Health between 2008 and 2014. The number of admissions due to dengue was compared to other potentially preventable hospitalizations. Hospital departments were categorized to reflect dengue-related activity as high dengue activity (HDA), low dengue activity (LDA), or zero dengue activity departments, and the impact of dengue activity on general in-hospital mortality in HDA departments was assessed. RESULTS: Dengue was the cause of more hospital admissions than most of the potentially preventable prevalent acute and chronic conditions and other infectious diseases. In HDA departments, dengue patient load was found to be a significant risk factor for overall in-hospital mortality. There was an approximately two-fold higher dengue case-fatality rate in LDA versus HDA departments, irrespective of dengue severity. CONCLUSIONS: This study confirms that dengue is an important cause of hospitalization in Mexico and highlights the impact of dengue activity not only on dengue case-fatality rate but also on the overall in-hospital mortality.

Comorbidities increase in-hospital mortality in dengue patients in Brazil.

Dengue remains an unmet public health burden. We determined risk factors for dengue in-hospital mortality in Brazil. Of 326,380 hospitalised dengue cases in 9-45-year-old individuals, there were 971 deaths. Risk of dying was 11-times higher in the presence of underlying common comorbidities (renal, infectious, pulmonary disease and diabetes), similar to the risk of dying from severe dengue and much higher with the combination. Ensuring access to integrated dengue preventative measures in individuals aged ≥ 9 years including those with comorbidities may help achieve the WHO objective of 50% reduction in mortality and 25% reduction in morbidity due to dengue by 2020.

Tetravalent Dengue Vaccine Reduces Symptomatic and Asymptomatic Dengue Virus Infections in Healthy Children and Adolescents Aged 2-16 Years in Asia and Latin America.

BACKGROUND: Asymptomatic dengue virus-infected individuals are thought to play a major role in dengue virus transmission. The efficacy of the recently approved quadrivalent CYD-TDV dengue vaccine against asymptomatic dengue virus infection has not been previously assessed. METHODS: We pooled data for 3 736 individuals who received either CYD-TDV or placebo at 0, 6, and 12 months in the immunogenicity subsets of 2 phase 3 trials (clinical trials registration NCT01373281 and NCT01374516). We defined a seroconversion algorithm (ie, a ≥4-fold increase in the neutralizing antibody titer and a titer of ≥40 from month 13 to month 25) as a surrogate marker of asymptomatic infection in the vaccine and placebo groups. RESULTS: The algorithm detected seroconversion in 94% of individuals with a diagnosis of virologically confirmed dengue between months 13 and 25, validating its discriminatory power. Among those without virologically confirmed dengue (n = 3 669), 219 of 2 485 in the vaccine group and 157 of 1 184 in the placebo group seroconverted between months 13 and 25, giving a vaccine efficacy of 33.5% (95% confidence interval [CI], 17.9%-46.1%) against asymptomatic infection. Vaccine efficacy was marginally higher in subjects aged 9-16 years (38.6%; 95% CI, 22.1%-51.5%). The annual incidence of asymptomatic dengue virus infection in this age group was 14.8%, which was 4.4 times higher than the incidence for symptomatic dengue (3.4%). CONCLUSIONS: The observed vaccine efficacy against asymptomatic dengue virus infections is expected to translate into reduced dengue virus transmission if sufficient individuals are vaccinated in dengue-endemic areas.

The Long-Term Safety, Public Health Impact, and Cost-Effectiveness of Routine Vaccination with a Recombinant, Live-Attenuated Dengue Vaccine (Dengvaxia): A Model Comparison Study.

BACKGROUND: Large Phase III trials across Asia and Latin America have recently demonstrated the efficacy of a recombinant, live-attenuated dengue vaccine (Dengvaxia) over the first 25 mo following vaccination. Subsequent data collected in the longer-term follow-up phase, however, have raised concerns about a potential increase in hospitalization risk of subsequent dengue infections, in particular among young, dengue-naïve vaccinees. We here report predictions from eight independent modelling groups on the long-term safety, public health impact, and cost-effectiveness of routine vaccination with Dengvaxia in a range of transmission settings, as characterised by seroprevalence levels among 9-y-olds (SP9). These predictions were conducted for the World Health Organization to inform their recommendations on optimal use of this vaccine. METHODS AND FINDINGS: The models adopted, with small variations, a parsimonious vaccine mode of action that was able to reproduce quantitative features of the observed trial data. The adopted mode of action assumed that vaccination, similarly to natural infection, induces transient, heterologous protection and, further, establishes a long-lasting immunogenic memory, which determines disease severity of subsequent infections. The default vaccination policy considered was routine vaccination of 9-y-old children in a three-dose schedule at 80% coverage. The outcomes examined were the impact of vaccination on infections, symptomatic dengue, hospitalised dengue, deaths, and cost-effectiveness over a 30-y postvaccination period. Case definitions were chosen in accordance with the Phase III trials. All models predicted that in settings with moderate to high dengue endemicity (SP9 ≥ 50%), the default vaccination policy would reduce the burden of dengue disease for the population by 6%-25% (all simulations: -3%-34%) and in high-transmission settings (SP9 ≥ 70%) by 13%-25% (all simulations: 10%- 34%). These endemicity levels are representative of the participating sites in both Phase III trials. In contrast, in settings with low transmission intensity (SP9 ≤ 30%), the models predicted that vaccination could lead to a substantial increase in hospitalisation because of dengue. Modelling reduced vaccine coverage or the addition of catch-up campaigns showed that the impact of vaccination scaled approximately linearly with the number of people vaccinated. In assessing the optimal age of vaccination, we found that targeting older children could increase the net benefit of vaccination in settings with moderate transmission intensity (SP9 = 50%). Overall, vaccination was predicted to be potentially cost-effective in most endemic settings if priced competitively. The results are based on the assumption that the vaccine acts similarly to natural infection. This assumption is consistent with the available trial results but cannot be directly validated in the absence of additional data. Furthermore, uncertainties remain regarding the level of protection provided against disease versus infection and the rate at which vaccine-induced protection declines. CONCLUSIONS: Dengvaxia has the potential to reduce the burden of dengue disease in areas of moderate to high dengue endemicity. However, the potential risks of vaccination in areas with limited exposure to dengue as well as the local costs and benefits of routine vaccination are important considerations for the inclusion of Dengvaxia into existing immunisation programmes. These results were important inputs into WHO global policy for use of this licensed dengue vaccine.

Some extensions in continuous models for immunological correlates of protection.

BACKGROUND: A scaled logit model has previously been proposed to quantify the relationship between an immunological assay and protection from disease, and has been applied in a number of settings. The probability of disease was modelled as a function of the probability of exposure, which was assumed to be fixed, and of protection, which was assumed to increase smoothly with the value of the assay. METHODS: Some extensions are here investigated. Alternative functions to represent the protection curve are explored, applications to case-cohort designs are evaluated, and approaches to variance estimation compared. The steepness of the protection curve must sometimes be bounded to achieve convergence and methods for doing so are outlined. Criteria for evaluating the fit of models are proposed and approaches to assessing the utility of results suggested. Models are evaluated by application to sixteen datasets from vaccine clinical trials. RESULTS: Alternative protection curve functions improved model evaluation criteria for every dataset. Standard errors based on the observed information were found to be unreliable; bootstrap estimates of precision were to be preferred. In most instances, case-cohort designs resulted in little loss of precision. Some results achieved suggested measures for utility. CONCLUSIONS: The original scaled logit model can be improved upon. Evaluation criteria permit well-fitting models and useful results to be identified. The proposed methods provide a comprehensive set of tools for quantifying the relationship between immunological assays and protection from disease.

Antibody Persistence and Risk of COVID-19 Infection: Insights from Modeling.

BACKGROUND: In this post hoc exploratory study of the APHP-COVIBOOST trial (NCT05124171), we used statistical modeling to describe the evolution of neutralizing antibody (nAb) titers over time, asses its impact on SARS-CoV-2 infection, and explore potential differences between three booster vaccine formulations (D614, B.1.351, and BNT162b2). METHODS: Antibody titers were measured for 208 adult participants at day 28, 3 months, and 6 months using a microneutralization assay against different Omicron subvariants. We developed four specific Bayesian statistical models based on a core model, accounting for vaccine-specific antibody decline, boosting of nAb for breakthrough infection, and risk of infection according to nAb levels. The model findings were cross-verified using another validated microneutralization assay. RESULTS: The decrease in nAb titers was significantly lower for the B.1.351 vaccine than for the other booster formulations. An inverse relationship was found between risk of infection upon exposure and nAb levels. With Omicron BA.1 data, these results translated into a positive relative vaccine efficacy against any infection over 6 months for the B.1.351 vaccine compared to the BNT162b2 (31%) and D614 (21%) vaccines. CONCLUSIONS: Risk of infection decreased with increasing nAb titers for all vaccines. Statistical models predicted significantly better antibody persistence for the B.1.351 booster formulation compared to the other evaluated vaccines.

Redefining pandemic preparedness: Multidisciplinary insights from the CERP modelling workshop in infectious diseases, workshop report.

In July 2023, the Center of Excellence in Respiratory Pathogens organized a two-day workshop on infectious diseases modelling and the lessons learnt from the Covid-19 pandemic. This report summarizes the rich discussions that occurred during the workshop. The workshop participants discussed multisource data integration and highlighted the benefits of combining traditional surveillance with more novel data sources like mobility data, social media, and wastewater monitoring. Significant advancements were noted in the development of predictive models, with examples from various countries showcasing the use of machine learning and artificial intelligence in detecting and monitoring disease trends. The role of open collaboration between various stakeholders in modelling was stressed, advocating for the continuation of such partnerships beyond the pandemic. A major gap identified was the absence of a common international framework for data sharing, which is crucial for global pandemic preparedness. Overall, the workshop underscored the need for robust, adaptable modelling frameworks and the integration of different data sources and collaboration across sectors, as key elements in enhancing future pandemic response and preparedness.

Preparing for the upcoming 2022/23 influenza season: A modelling study of the susceptible population in Australia, France, Germany, Italy, Spain and the United Kingdom.

We analysed the influenza epidemic that occurred in Australia during the 2022 winter using an age-structured dynamic transmission model, which accounts for past epidemics to estimate the population susceptibility to an influenza infection. We applied the same model to five European countries. Our analysis suggests Europe might experience an early and moderately large influenza epidemic. Also, differences may arise between countries, with Germany and Spain experiencing larger epidemics, than France, Italy and the United Kingdom, especially in children.

Development of an early alert model for pandemic situations in Germany.

The COVID-19 pandemic has pointed out the need for new technical approaches to increase the preparedness of healthcare systems. One important measure is to develop innovative early warning systems. Along those lines, we first compiled a corpus of relevant COVID-19 related symptoms with the help of a disease ontology, text mining and statistical analysis. Subsequently, we applied statistical and machine learning (ML) techniques to time series data of symptom related Google searches and tweets spanning the time period from March 2020 to June 2022. In conclusion, we found that a long-short-term memory (LSTM) jointly trained on COVID-19 symptoms related Google Trends and Twitter data was able to accurately forecast up-trends in classical surveillance data (confirmed cases and hospitalization rates) 14 days ahead. In both cases, F1 scores were above 98% and 97%, respectively, hence demonstrating the potential of using digital traces for building an early alert system for pandemics in Germany.

How did the adoption of wP-pentavalent affect the global paediatric vaccine coverage rate? A multicountry panel data analysis.

OBJECTIVES: Several studies have highlighted the effects of combination vaccines on immunisation coverage at the national or subnational level. This study examined the effects globally. Worldwide introduction of whole-cell pertussis pentavalent (wP-pentavalent) allowed estimation of incremental coverage effects of combination vaccines on the third doses of diphtheria, tetanus, pertussis (DTP3); hepatitis B (HepB3) and Haemophilus influenzae type B (Hib3). DESIGN: Multicountry panel data analysis. DATA SOURCES: Country-level vaccine coverage data of WHO/UNICEF for the years 1980-2018. METHODS: Linear mixed models were used to estimate the effects of wP-pentavalent introduction by incorporating proxy variables to control for time trend and other time-dependent changes in the immunisation programmes. RESULTS: Introduction of combination vaccines may have improved the coverage of DTP3 by 3percentage points(95% CI 2.5% to 3.6%) globally compared with the coverage in the pre-combination vaccine era. The comparison of coverage rates of HepB3 and Hib3 in before and after wP-pentavalent periods indicates that the introduction of combination vaccines improved the coverage by 10.1 percentage points (95% CI 8.4% to 11.7%) for HepB3 and 9.9 (95% CI 7.1% to 12.7%) for Hib3 in countries that introduced those antigens prior to adoption of wP-pentavalent. Even though the incremental coverage increase of DTP3 appears quite modest, it is still a significant result, especially because DTP vaccine has been in the national immunisation programmes of all countries for about 24 years prior to the introduction of wP-pentavalent. Additionally, the introduction of pentavalent also allowed inclusion of Hib and HepB in the vaccine schedule for a large number of countries (85 and 37, respectively, of the 102 countries included in our analysis). CONCLUSION: The findings suggest that development of combination vaccines with additional antigens is likely to help sustain and improve coverage of existing as well as new childhood vaccines.

Public health impact and cost-effectiveness of implementing a 'pre-vaccination screening' strategy with the dengue vaccine in Puerto Rico.

BACKGROUND: The World Health Organization (WHO) recommended 'pre-vaccination screening' as its preferred implementation strategy when using the licensed dengue vaccine (CYD-TDV; Dengvaxia, Sanofi), so that only individuals with previous dengue infection are vaccinated. The US Centers for Disease Control and Prevention (CDC) recommended use of CYD-TDV to prevent dengue in children with previous laboratory-confirmed dengue infection in regions where dengue is endemic. Here, we evaluate the public health impact and cost-effectiveness of a 'pre-vaccination screening' strategy in Puerto Rico. METHODS: The current analysis builds upon a previously published transmission model used to assess the benefits/risks associated with dengue vaccination. For 'pre-vaccination screening', three alternative testing methods were assessed: one using an immunoglobulin G (IgG) enzyme-linked immunosorbent assay (ELISA) dengue serotest, another with dengue serotesting using a rapid diagnostic test (RDT), and one using both sequentially (as recommended in Puerto Rico). The time horizon considered was 10 years. RESULTS: In Puerto Rico, the disability-adjusted life years (DALYs) averted for 'pre-vaccination screening' with an ELISA-based program, RDT-based program, and both sequentially would be a median 1,192 (95% CI: 716-2,232), 2,812 (95% CI: 1,579-5,019), and 1,017 (95% CI: 561-1,738), respectively. These benefits would arise from the reduction in cases: median 24,961 (95% CI: 17,480-36,782), 58,273 (95% CI: 40,729-84,796), 20,775 (95% CI: 14,637-30,374) fewer cases, respectively. The cost per DALY averted from a payer perspective would be US$12,518 (95 %CI: US$4,749-26,922), US$10,047 (95% CI: US$3,350-23,852), and US$12,334 (95% CI: US$4,965-26,444), respectively. All three strategies would be cost saving from a societal perspective. CONCLUSIONS: Our study supports the WHO and CDC 'pre-vaccination screening' guidance for CYD-TDV implementation. In Puerto Rico, regardless of the testing strategy and even with a relatively low rate of testing, it would be cost-effective from a payer perspective and cost saving from a societal perspective.

Estimating dengue transmission intensity from serological data: A comparative analysis using mixture and catalytic models.

BACKGROUND: Dengue virus (DENV) infection is a global health concern of increasing magnitude. To target intervention strategies, accurate estimates of the force of infection (FOI) are necessary. Catalytic models have been widely used to estimate DENV FOI and rely on a binary classification of serostatus as seropositive or seronegative, according to pre-defined antibody thresholds. Previous work has demonstrated the use of thresholds can cause serostatus misclassification and biased estimates. In contrast, mixture models do not rely on thresholds and use the full distribution of antibody titres. To date, there has been limited application of mixture models to estimate DENV FOI. METHODS: We compare the application of mixture models and time-constant and time-varying catalytic models to simulated data and to serological data collected in Vietnam from 2004 to 2009 (N ≥ 2178) and Indonesia in 2014 (N = 3194). RESULTS: The simulation study showed larger mean FOI estimate bias from the time-constant and time-varying catalytic models (-0.007 (95% Confidence Interval (CI): -0.069, 0.029) and -0.006 (95% CI -0.095, 0.043)) than from the mixture model (0.001 (95% CI -0.036, 0.065)). Coverage of the true FOI was > 95% for estimates from both the time-varying catalytic and mixture model, however the latter had reduced uncertainty. When applied to real data from Vietnam, the mixture model frequently produced higher FOI and seroprevalence estimates than the catalytic models. CONCLUSIONS: Our results suggest mixture models represent valid, potentially less biased, alternatives to catalytic models, which could be particularly useful when estimating FOI from data with largely overlapping antibody titre distributions.

Modeling approaches for early warning and monitoring of pandemic situations as well as decision support.

The COVID-19 pandemic has highlighted the lack of preparedness of many healthcare systems against pandemic situations. In response, many population-level computational modeling approaches have been proposed for predicting outbreaks, spatiotemporally forecasting disease spread, and assessing as well as predicting the effectiveness of (non-) pharmaceutical interventions. However, in several countries, these modeling efforts have only limited impact on governmental decision-making so far. In light of this situation, the review aims to provide a critical review of existing modeling approaches and to discuss the potential for future developments.

Hospital admissions with influenza and impact of age and comorbidities on severe clinical outcomes in Brazil and Mexico.

BACKGROUND: The risk of hospitalization or death after influenza infection is higher at the extremes of age and in individuals with comorbidities. We estimated the number of hospitalizations with influenza and characterized the cumulative risk of comorbidities and age on severe outcomes in Mexico and Brazil. METHODS: We used national hospital discharge data from Brazil (SIH/SUS) from 2010-2018 and Mexico (SAEH) from 2010-2017 to estimate the number of influenza admissions using ICD-10 discharge codes, stratified by age (0-4, 5-17, 18-49, 50-64, and ≥65 years). Duration of hospital stay, admission to the intensive care unit (ICU), and in-hospital case fatality rates (CFRs) defined the severe outcomes. Rates were compared between patients with or without pre-specified comorbidities and by age. RESULTS: A total of 327,572 admissions with influenza were recorded in Brazil and 20,613 in Mexico, with peaks period most years. In Brazil, the median hospital stay duration was 3.0 days (interquartile range, 2.0-5.0), ICU admission rate was 3.3% (95% CI, 3.2-3.3%), and in-hospital CFR was 4.6% (95% CI, 4.5-4.7). In Mexico, the median duration of stay was 5.0 days (interquartile range, 3.0-7.0), ICU admission rate was 1.8% (95% CI, 1.6-2.0%), and in-hospital CFR was 6.9% (95% CI, 6.5-7.2). In Brazil, ICU admission and in-hospital CFR were higher in adults aged ≥50 years and increased in the presence of comorbidities, especially cardiovascular disease. In Mexico, comorbidities increased the risk of ICU admission by 1.9 (95% CI, 1.0-3.5) and in-hospital CFR by 13.9 (95% CI, 8.4-22.9) in children 0-4 years. CONCLUSION: The SIH/SUS and SAEH databases can be used to estimate hospital admissions with influenza, and the disease severity. Age and comorbidities, especially cardiovascular disease, are cumulatively associated with more severe outcomes, with differences between countries. This association should be further analyzed in prospective surveillance studies designed to support influenza vaccination strategy decisions.

Estimating social contacts in mass gatherings for disease outbreak prevention and management: case of Hajj pilgrimage.

BACKGROUND: Most mass gathering events have been suspended due to the SARS-CoV-2 pandemic. However, with vaccination rollout, whether and how to organize some of these mass gathering events arises as part of the pandemic recovery discussions, and this calls for decision support tools. The Hajj, one of the world's largest religious gatherings, was substantively scaled down in 2020 and 2021 and it is still unclear how it will take place in 2022 and subsequent years. Simulating disease transmission dynamics during the Hajj season under different conditions can provide some insights for better decision-making. Most disease risk assessment models require data on the number and nature of possible close contacts between individuals. METHODS: We sought to use integrated agent-based modeling and discrete events simulation techniques to capture risky contacts among the pilgrims and assess different scenarios in one of the Hajj major sites, namely Masjid-Al-Haram. RESULTS: The simulation results showed that a plethora of risky contacts may occur during the rituals. Also, as the total number of pilgrims increases at each site, the number of risky contacts increases, and physical distancing measures may be challenging to maintain beyond a certain number of pilgrims in the site. CONCLUSIONS: This study presented a simulation tool that can be relevant for the risk assessment of a variety of (respiratory) infectious diseases, in addition to COVID-19 in the Hajj season. This tool can be expanded to include other contributing elements of disease transmission to quantify the risk of the mass gathering events.