Epidemic Trajectories Research Articles

COVINet: a deep learning-based and interpretable prediction model for the county-wise trajectories of COVID-19 in the United States

The devastating impact of COVID-19 on the United States has been profound since its onset in January 2020. Predicting the trajectory of epidemics accurately and devising strategies to curb their progression are currently formidable challenges. In response to this crisis, we propose COVINet, which combines the architecture of Long Short-Term Memory and Gated Recurrent Unit, incorporating actionable covariates to offer high-accuracy prediction and explainable response. First, we train COVINet models for confirmed cases and total deaths with five input features, and compare Mean Absolute Errors (MAEs) and Mean Relative Errors (MREs) of COVINet against ten competing models from the United States CDC in the last four weeks before April 26, 2021. The results show COVINet outperforms all competing models for MAEs and MREs when predicting total deaths. Then, we focus on prediction for the most severe county in each of the top 10 hot-spot states using COVINet. The MREs are small for all predictions made in the last 7 or 30 days before March 23, 2023. Beyond predictive accuracy, COVINet offers high interpretability, enhancing the understanding of pandemic dynamics. This dual capability positions COVINet as a powerful tool for informing effective strategies in pandemic prevention and governmental decision-making.

Kinetics of Viral Shedding for Outbreak Surveillance of Emerging Infectious Diseases: Modeling Approach to SARS-CoV-2 Alpha and Omicron Infection.

Previous studies have highlighted the importance of viral shedding using cycle threshold (Ct) values obtained via reverse transcription polymerase chain reaction to understand the epidemic trajectories of SARS-CoV-2 infections. However, it is rare to elucidate the transition kinetics of Ct values from the asymptomatic or presymptomatic phase to the symptomatic phase before recovery using individual repeated Ct values. This study proposes a novel Ct-enshrined compartment model to provide a series of quantitative measures for delineating the full trajectories of the dynamics of viral load from infection until recovery. This Ct-enshrined compartment model was constructed by leveraging Ct-classified states within and between presymptomatic and symptomatic compartments before recovery or death among people with infections. A series of recovery indices were developed to assess the net kinetic movement of Ct-up toward and Ct-down off recovery. The model was applied to (1) a small-scale community-acquired Alpha variant outbreak under the "zero-COVID-19" policy without vaccines in May 2021 and (2) a large-scale community-acquired Omicron variant outbreak with high booster vaccination rates following the lifting of the "zero-COVID-19" policy in April 2022 in Taiwan. The model used Bayesian Markov chain Monte Carlo methods with the Metropolis-Hastings algorithm for parameter estimation. Sensitivity analyses were conducted by varying Ct cutoff values to assess the robustness of the model. The kinetic indicators revealed a marked difference in viral shedding dynamics between the Alpha and Omicron variants. The Alpha variant exhibited slower viral shedding and lower recovery rates, but the Omicron variant demonstrated swifter viral shedding and higher recovery rates. Specifically, the Alpha variant showed gradual Ct-up transitions and moderate recovery rates, yielding a presymptomatic recovery index slightly higher than 1 (1.10), whereas the Omicron variant had remarkable Ct-up transitions and significantly higher asymptomatic recovery rates, resulting in a presymptomatic recovery index much higher than 1 (152.5). Sensitivity analysis confirmed the robustness of the chosen Ct values of 18 and 25 across different recovery phases. Regarding the impact of vaccination, individuals without booster vaccination had a 19% higher presymptomatic incidence rate compared to those with booster vaccination. Breakthrough infections in boosted individuals initially showed similar Ct-up transition rates but higher rates in later stages compared to nonboosted individuals. Overall, booster vaccination improved recovery rates, particularly during the symptomatic phase, although recovery rates for persistent asymptomatic infection were similar regardless of vaccination status once the Ct level exceeded 25. The study provides new insights into dynamic Ct transitions, with the notable finding that Ct-up transitions toward recovery outpaced Ct-down and symptom-surfacing transitions during the presymptomatic phase. The Ct-up against Ct-down transition varies with variants and vaccination status. The proposed Ct-enshrined compartment model is useful for the surveillance of emerging infectious diseases in the future to prevent community-acquired outbreaks.

Estimating epidemic trajectories of SARS-CoV-2 and influenza A virus based on wastewater monitoring and a novel machine learning algorithm

The COVID-19 pandemic has altered the circulation of non-SARS-CoV-2 respiratory viruses. In this study, we carried out wastewater surveillance of SARS-CoV-2 and influenza A virus (IAV) in three key port cities in China through real-time quantitative PCR (RT–qPCR). Next, a novel machine learning algorithm (MLA) based on Gaussian model and random forest model was used to predict the epidemic trajectories of SARS-CoV-2 and IAV. The results showed that from February 2023 to January 2024, three port cities experienced two waves of SARS-CoV-2 infection, which peaked in late-May and late-August 2023, respectively. Two waves of IAV were observed in the spring and winter of 2023, respectively with considerable variations in terms of onset/offset date and duration. Furthermore, we employed MLA to extract the key features of epidemic trajectories of SARS-CoV-2 and IAV from February 3rd, to October 15th, 2023, and thereby predicted the epidemic trends of SARS-CoV-2 and IAV from October 16th, 2023 to April 22nd, 2024, which showed high consistency with the observed values. These collective findings offer an important understanding of SARS-CoV-2 and IAV epidemics, suggesting that wastewater surveillance together with MLA emerges as a powerful tool for risk assessment of respiratory viral diseases and improving public health preparedness.

Wastewater Surveillance Uncovered the Impacts of the Timing of COVID-19 on the Epidemic Trajectories of Other Respiratory Diseases in Two Northeastern Cities in China

Wastewater Surveillance Uncovered the Impacts of the Timing of COVID-19 on the Epidemic Trajectories of Other Respiratory Diseases in Two Northeastern Cities in China

Building a network with assortative mixing starting from preference functions, with application to the spread of epidemics

Compartmental models of disease spread have been well studied on networks built according to the Configuration Model, i.e., where the degree distribution of individual nodes is specified, but where connections are made randomly. Dynamics of spread on such “first order” networks were shown to be profoundly different compared to epidemics under the traditional mass action assumption. Assortativity, i.e., the preferential mixing of nodes according to degree, is a second order property that is thought to impact epidemic trajectory. We first show how assortative mixing can come about from individual preferences to connect with others of lower or higher degree, and propose an algorithm for constructing such a network. We then investigate via simulation how this network structure favors or inhibits diffusion processes, such as the spread of an infectious disease.

Canada's provincial COVID-19 pandemic modelling efforts: A review of mathematical models and their impacts on the responses.

Mathematical modelling played an important role in the public health response to COVID-19 in Canada. Variability in epidemic trajectories, modelling approaches, and data infrastructure across provinces provides a unique opportunity to understand the factors that shaped modelling strategies. Provinces implemented stringent pandemic interventions to mitigate SARS-CoV-2 transmission, considering evidence from epidemic models. This study aimed to summarize provincial COVID-19 modelling efforts. We identified modelling teams working with provincial decision-makers, through referrals and membership in Canadian modelling networks. Information on models, data sources, and knowledge translation were abstracted using standardized instruments. We obtained information from six provinces. For provinces with sustained community transmission, initial modelling efforts focused on projecting epidemic trajectoriesand healthcare demands, and evaluating impacts of proposed interventions. In provinces with low community transmission, models emphasized quantifying importation risks. Most of the models were compartmental and deterministic, with projection horizons of a few weeks. Models were updated regularly or replaced by new ones, adapting to changing local epidemic dynamics, pathogen characteristics, vaccines, and requests from public health. Surveillance datasets for cases, hospitalizations and deaths, and serological studies were the main data sources for model calibration. Access to data for modelling and the structure for knowledge translation differed markedly between provinces. Provincial modelling efforts during the COVID-19 pandemic were tailored to local contexts and modulated by available resources. Strengthening Canadian modelling capacity, developing and sustaining collaborations between modellers and governments, and ensuringearlier access to linked and timely surveillance data could help improve pandemic preparedness.

Evaluating the forecasting performance of ensemble sub-epidemic frameworks and other time series models for the 2022-2023 mpox epidemic.

During the 2022-2023 unprecedented mpox epidemic, near real-time short-term forecasts of the epidemic's trajectory were essential in intervention implementation and guiding policy. However, as case levels have significantly decreased, evaluating model performance is vital to advancing the field of epidemic forecasting. Using laboratory-confirmed mpox case data from the Centers for Disease Control and Prevention and Our World in Data teams, we generated retrospective sequential weekly forecasts for Brazil, Canada, France, Germany, Spain, the United Kingdom, the United States and at the global scale using an auto-regressive integrated moving average (ARIMA) model, generalized additive model, simple linear regression, Facebook's Prophet model, as well as the sub-epidemic wave and n-sub-epidemic modelling frameworks. We assessed forecast performance using average mean squared error, mean absolute error, weighted interval scores, 95% prediction interval coverage, skill scores and Winkler scores. Overall, the n-sub-epidemic modelling framework outcompeted other models across most locations and forecasting horizons, with the unweighted ensemble model performing best most frequently. The n-sub-epidemic and spatial-wave frameworks considerably improved in average forecasting performance relative to the ARIMA model (greater than 10%) for all performance metrics. Findings further support sub-epidemic frameworks for short-term forecasting epidemics of emerging and re-emerging infectious diseases.

Emergence of the reproduction matrix in epidemic forecasting.

During the recent COVID-19 pandemic, the instantaneous reproduction number, R(t), has surged as a widely used measure to target public health interventions aiming at curbing the infection rate. In analogy with the basic reproduction number that arises from the linear stability analysis, R(t) is typically interpreted as a threshold parameter that separates exponential growth (R(t) > 1) from exponential decay (R(t) < 1). In real epidemics, however, the finite number of susceptibles, the stratification of the population (e.g. by age or vaccination state), and heterogeneous mixing lead to more complex epidemic courses. In the context of the multidimensional renewal equation, we generalize the scalar R(t) to a reproduction matrix, [Formula: see text], which details the epidemic state of the stratified population, and offers a concise epidemic forecasting scheme. First, the reproduction matrix is computed from the available incidence data (subject to some a priori assumptions), then it is projected into the future by a transfer functional to predict the epidemic course. We demonstrate that this simple scheme allows realistic and accurate epidemic trajectories both in synthetic test cases and with reported incidence data from the COVID-19 pandemic. Accounting for the full heterogeneity and nonlinearity of the infection process, the reproduction matrix improves the prediction of the infection peak. In contrast, the scalar reproduction number overestimates the possibility of sustaining the initial infection rate and leads to an overshoot in the incidence peak. Besides its simplicity, the devised forecasting scheme offers rich flexibility to be generalized to time-dependent mitigation measures, contact rate, infectivity and vaccine protection.

Impact of vaccine coverage and disruption to health services on COVID-19 in Ukraine

COVID-19 surveillance in Ukraine ceased after the Russian invasion of the country in 2022, on a background of low vaccination rates of 34.5% for two doses at this time. We conducted a modelling study to estimate the epidemic trajectory of SARS-COV-2 in Ukraine after the start of the war. We use a COVID-19 deterministic Susceptible-Exposed-Infected-Recovered (SEIR) model for Ukraine to estimate the impact of increased vaccination coverage and masking as public health interventions. We fit the model output to case notification data between 6 January and 25 February 2022, then we forecast the COVID-19 epidemic trajectory in different scenarios of mask use and vaccine coverage. In the best-case scenario, 69% of the Ukrainian population would have been infected in the first half of 2022. Increasing mask use from 50 to 80% reduces cases and deaths by 17% and 30% respectively, while increasing vaccination rates to 60% and 9.6% for two and three doses respectively results in a 3% reduction in cases and 28% in deaths. However, if vaccination is increased to a higher coverage of 80% with two doses and 12.8% with three, or mask effectiveness is reduced to 40%, increasing vaccination coverage is more effective. The loss of health services, displacement, and destruction of infrastructure will amplify the risk of COVID-19 in Ukraine and make vaccine programs less feasible. Masks do not need the health infrastructure or cold-chain logistics required for vaccines and are more feasible for rapid epidemic control during war. However, increasing vaccine coverage will save more lives. Vaccination of refugees who have fled to other countries can be more feasibly achieved.

Limited data on infectious disease distribution exposes ambiguity in epidemic modeling choices

Traditional disease transmission models assume that the infectious period is exponentially distributed with a recovery rate fixed in time and across individuals. This assumption provides analytical and computational advantages, however, it is often unrealistic when compared to empirical data. Current efforts in modeling nonexponentially distributed infectious periods are either limited to special cases or lead to unsolvable models. Also, the link between empirical data (the infectious period distribution) and the modeling needs (the definition of the corresponding recovery rates) lacks a clear understanding. Here we introduce a mapping of an arbitrary distribution of infectious periods into a distribution of recovery rates. Under the Markovian assumption to ensure analytical tractability, we show that the same infectious period distribution at the population level can be reproduced by two modeling schemes that we call and , depending on the individual response to the infection, and aggregated empirical data cannot easily discriminate the correct scheme. Besides being conceptually different, the two schemes also lead to different epidemic trajectories. Although sharing the same behavior close to the disease-free equilibrium, the scheme deviates from the expected epidemic when reaching the endemic equilibrium of a susceptible-infectious-susceptible transmission model, while the scheme turns out to be equivalent to assuming a homogeneous recovery rate. We show this through analytical computations and stochastic epidemic simulations on a contact network, using both generative network models and empirical contact data. It is therefore possible to reproduce heterogeneous infectious periods in network-based transmission models, however, the resulting prevalence is sensitive to the modeling choice for the interpretation of the empirically collected data on the length of the infectious period. In the absence of higher resolution data, studies should acknowledge such deviations in the epidemic predictions. Published by the American Physical Society 2024

Detecting changes in population trends in infection surveillance using community SARS-CoV-2 prevalence as an exemplar.

Detecting and quantifying changes in growth rates of infectious diseases is vital to informing public health strategy and can inform policymakers' rationale for implementing or continuing interventions aimed at reducing impact. Substantial changes in SARS-CoV-2 prevalence with emergence of variants provides opportunity to investigate different methods to do this. We included PCR results from all participants in the UK's COVID-19 Infection Survey between August 2020-June 2022. Change-points for growth rates were identified using iterative sequential regression (ISR) and second derivatives of generalised additive models (GAMs). Consistency between methods and timeliness of detection were compared. Of 8,799,079 visits, 147,278 (1.7%) were PCR-positive. Change-points associated with emergence of major variants were estimated to occur a median 4 days earlier (IQR 0-8) in GAMs versus ISR. When estimating recent change-points using successive data periods, four change-points (4/96) identified by GAMs were not found when adding later data or by ISR. Change-points were detected 3-5 weeks after they occurred in both methods but could be detected earlier within specific subgroups. Change-points in growth rates of SARS-CoV-2 can be detected in near real-time using ISR and second derivatives of GAMs. To increase certainty about changes in epidemic trajectories both methods could be run in parallel.

Combining models to generate consensus medium-term projections of hospital admissions, occupancy and deaths relating to COVID-19 in England.

Mathematical modelling has played an important role in offering informed advice during the COVID-19 pandemic. In England, a cross government and academia collaboration generated medium-term projections (MTPs) of possible epidemic trajectories over the future 4-6 weeks from a collection of epidemiological models. In this article, we outline this collaborative modelling approach and evaluate the accuracy of the combined and individual model projections against the data over the period November 2021-December 2022 when various Omicron subvariants were spreading across England. Using a number of statistical methods, we quantify the predictive performance of the model projections for both the combined and individual MTPs, by evaluating the point and probabilistic accuracy. Our results illustrate that the combined MTPs, produced from an ensemble of heterogeneous epidemiological models, were a closer fit to the data than the individual models during the periods of epidemic growth or decline, with the 90% confidence intervals widest around the epidemic peaks. We also show that the combined MTPs increase the robustness and reduce the biases associated with a single model projection. Learning from our experience of ensemble modelling during the COVID-19 epidemic, our findings highlight the importance of developing cross-institutional multi-model infectious disease hubs for future outbreak control.

Assessment of sewer connectivity in the United States and its implications for equity in wastewater-based epidemiology.

Wastewater-based epidemiology is a promising public health tool that can yield a more representative view of the population than case reporting. However, only about 80% of the U.S. population is connected to public sewers, and the characteristics of populations missed by wastewater-based epidemiology are unclear. To address this gap, we used publicly available datasets to assess sewer connectivity in the U.S. by location, demographic groups, and economic groups. Data from the U.S. Census' American Housing Survey revealed that sewer connectivity was lower than average when the head of household was American Indian and Alaskan Native, White, non-Hispanic, older, and for larger households and those with higher income, but smaller geographic scales revealed local variations from this national connectivity pattern. For example, data from the U.S. Environmental Protection Agency showed that sewer connectivity was positively correlated with income in Minnesota, Florida, and California. Data from the U.S. Census' American Community Survey and Environmental Protection Agency also revealed geographic areas with low sewer connectivity, such as Alaska, the Navajo Nation, Minnesota, Michigan, and Florida. However, with the exception of the U.S. Census data, there were inconsistencies across datasets. Using mathematical modeling to assess the impact of wastewater sampling inequities on inferences about epidemic trajectory at a local scale, we found that in some situations, even weak connections between communities may allow wastewater monitoring in one community to serve as a reliable proxy for an interacting community with no wastewater monitoring, when cases are widespread. A systematic, rigorous assessment of sewer connectivity will be important for ensuring an equitable and informed implementation of wastewater-based epidemiology as a public health monitoring system.

Future HIV epidemic trajectories in South Africa and projected long-term consequences of reductions in general population HIV testing: a mathematical modelling study

After successful intensive interventions to rapidly increase HIV awareness, coverage of antiretroviral therapy (ART), and viral suppression, HIV programmes in eastern and southern Africa are considering scaling back of some interventions, such as widespread general population HIV testing. We aimed to model whether scaling back of general population HIV testing in South Africa could result in a resurgence of the HIV epidemic or substantial slowing of declines in HIV incidence, resulting in increased long-term ART. In this modelling study, we used the Thembisa 4.5 model (a deterministic compartmental model of HIV transmission in South Africa) to project the South African HIV epidemic to 2100 assuming the continuation of 2022 epidemiological conditions and HIV programme implementation. We assessed how implementing reductions in general population HIV testing services in 2025 (while maintaining antenatal, symptom-based, and risk-based testing modalities and other HIV prevention services at 2022 levels) would affect HIV incidence and prevalence among people aged 15-49 years, the year in which incidence would reach one per 1000 people aged 15-49 years (the threshold for virtual elimination of HIV), and associated costs, as well as numbers of additional new HIV infections and AIDS-related deaths. We also modelled the effects of delaying reductions in general population testing services by 5-year increments. Additionally, we modelled the potential effects of reductions in general population testing services in combination with increases or decreases in ART interruption rates (ie, the annual rate at which people who are on ART discontinue ART) and condom usage in 2025-35. If general population HIV testing services and the HIV risk environment of 2022 were maintained, we projected that HIV incidence would steadily decline from 4·95 (95% CI 4·40-5·34) per 1000 population in 2025 to 0·14 (0·05-0·31) per 1000 in 2100, and that the so-called virtual elimination threshold of less than one new infection per 1000 population per year would be reached in 2055 (95% CI 2051-2060). Scaling back of general population HIV testing services by 25%, 50%, or 75% in 2025 delayed time to reaching the virtual elimination threshold by 5, 13, or 35 years, respectively, whereas complete cessation of general population testing would result in the threshold not being attained by 2100. Although the incidence of HIV continued to fall when general HIV testing services were reduced, our modelling suggested that, with reductions of between 25% and 100%, between 396 000 (95% CI 299 000-474 000) and 2·50 million (1·97 million-2·98 million) additional HIV infections and between 115 000 (94 000-135 000) and 795 000 (670 000-926 000) additional AIDS-related deaths would occur between 2025 and 2075, depending on the extent of reduction in testing. Delaying reductions in general population HIV testing services for 5-25 years mitigated some of these effects. HIV testing accounted for only 5% of total programmatic costs at baseline; reducing testing moderately reduced short-term total annual costs, but increased annual costs after 25 years. Increases in ART interruption and reductions in condom usage were projected to slow the decline in incidence and increase the coverage of general HIV testing services required to control transmission but did not cause rapid resurgence in HIV infections. Our modelling suggests that scaling back of general population HIV testing would not result in a resurgence of HIV infections, but would delay attainment of incidence-reduction targets and result in long-term increases in HIV infections, AIDS-related deaths, and costs (via increased need for ART provision). HIV programmes need to balance short-term potential resource savings with long-term epidemic control objectives. Bill & Melinda Gates Foundation.

Local-scale phylodynamics reveal differential community impact of SARS-CoV-2 in a metropolitan US county.

SARS-CoV-2 transmission is largely driven by heterogeneous dynamics at a local scale, leaving local health departments to design interventions with limited information. We analyzed SARS-CoV-2 genomes sampled between February 2020 and March 2022 jointly with epidemiological and cell phone mobility data to investigate fine scale spatiotemporal SARS-CoV-2 transmission dynamics in King County, Washington, a diverse, metropolitan US county. We applied an approximate structured coalescent approach to model transmission within and between North King County and South King County alongside the rate of outside introductions into the county. Our phylodynamic analyses reveal that following stay-at-home orders, the epidemic trajectories of North and South King County began to diverge. We find that South King County consistently had more reported and estimated cases, COVID-19 hospitalizations, and longer persistence of local viral transmission when compared to North King County, where viral importations from outside drove a larger proportion of new cases. Using mobility and demographic data, we also find that South King County experienced a more modest and less sustained reduction in mobility following stay-at-home orders than North King County, while also bearing more socioeconomic inequities that might contribute to a disproportionate burden of SARS-CoV-2 transmission. Overall, our findings suggest a role for local-scale phylodynamics in understanding the heterogeneous transmission landscape.

Deterministic epidemic models overestimate the basic reproduction number of observed outbreaks

The basic reproduction number, R0, is a well-known quantifier of epidemic spread. However, a class of existing methods for estimating R0 from incidence data early in the epidemic can lead to an over-estimation of this quantity. In particular, when fitting deterministic models to estimate the rate of spread, we do not account for the stochastic nature of epidemics and that, given the same system, some outbreaks may lead to epidemics and some may not. Typically, an observed epidemic that we wish to control is a major outbreak. This amounts to implicit selection for major outbreaks which leads to the over-estimation problem. We formally characterised the split between major and minor outbreaks by using Otsu's method which provides us with a working definition. We show that by conditioning a ‘deterministic’ model on major outbreaks, we can more reliably estimate the basic reproduction number from an observed epidemic trajectory.

Ending the HIV Epidemic in Black America: Qualitative Insights Following COVID-19.

The disproportionate effects of the human immunodeficiency virus (HIV) and the Coronavirus 2019 (COVID-19) on Black American communities highlight structural systems rooted in racism and must be addressed with national strategies that improve both biomedicine and social determinants of health. The purpose of this study was to qualitatively examine the experiences and interpretations of experts in the HIV workforce (local, state, and national HIV-related organizations) regarding the state of HIV and COVID-19 among Black Americans. Within key informant interviews and a focus group recorded and transcribed verbatim, fifteen members of the HIV workforce and Black community described their experiences and provided insights to inform ending the negative outcomes resulting from HIV and COVID-19. Data were analyzed using NVivo software, and eight themes emerged to address disease disproportionality through a Black lens. Themes reflected (1) accessing information and care; (2) key potential partners/stakeholders; (3) investing in Black communities; (4) governmental support; (5) increasing engagement and advocacy; (6) HIV-related community conversations; (7) developments since COVID-19; and (8) the Ending the HIV Epidemic (EHE) trajectory. Themes directly speak to recommendations to adjust education and policy strategies for HIV and COVID-19 prevention and intervention. Such recommendations, (1) amplifying Black voices, (2) investing sustainable dollars into Black communities, and (3) leaning into advocacy, can bolster the foundation for the HIV workforce and Black community to break ineffective response patterns and lead the fight against these systemic issues of inequity.

Flexible Bayesian inference on partially observed epidemics.

Individual-based models of contagious processes are useful for predicting epidemic trajectories and informing intervention strategies. In such models, the incorporation of contact network information can capture the non-randomness and heterogeneity of realistic contact dynamics. In this article, we consider Bayesian inference on the spreading parameters of an SIR contagion on a known, static network, where information regarding individual disease status is known only from a series of tests (positive or negative disease status). When the contagion model is complex or information such as infection and removal times is missing, the posterior distribution can be difficult to sample from. Previous work has considered the use of Approximate Bayesian Computation (ABC), which allows for simulation-based Bayesian inference on complex models. However, ABC methods usually require the user to select reasonable summary statistics. Here, we consider an inference scheme based on the Mixture Density Network compressed ABC, which minimizes the expected posterior entropy in order to learn informative summary statistics. This allows us to conduct Bayesian inference on the parameters of a partially observed contagious process while also circumventing the need for manual summary statistic selection. This methodology can be extended to incorporate additional simulation complexities, including behavioural change after positive tests or false test results.

The need for pre-emptive control strategies for mpox in Asia and Oceania

IntroductionThe transmission dynamics of the recent mpox outbreak highlights the lack of infrastructure available to rapidly respond to novel STI outbreaks, of which Asia and Oceania remains particularly susceptible. Here, we simulate outbreaks in this setting and propose the use of pre-emptive vaccination within the men who have sex with men (MSM) community before the arrival and establishment of the virus. Materials and methodsUsing data driven heterogeneous sexual contact networks, we simulated outbreaks of mpox in Singapore, Hong Kong, and Sydney. An individual based SEIR compartmental model was used to simulate epidemic trajectories and the impact of different vaccination uptakes was assessed in their ability to avert or suppress outbreaks upon the arrival of mpox within the MSM populations. ResultsThe highly dense sexual networks of Singapore and Sydney experience rapid outbreaks, with infection peaks occurring at day 41 and 23 respectively, compared to Hong Kong which occurs at day 77. Across the simulations with no vaccination, 68.2%–89.7% of the MSM community will become infected with mpox across the different cities, over a simulation period of 1 year. By implementing vaccination strategies, the infection rate across the cities can be reduced to as low as 3.1% of the population (range: 3.1%–82.2%) depending on the implementation and uptake of the vaccine. Vaccination is also extremely effective in slowing the start of the epidemic, delaying the epidemic peak by 36–50 days in Hong Kong, or even preventing the outbreak of mpox. DiscussionWith extremely dense and well-connected sexual contact networks, where 65.2%–83.2% of the population are connected to a super-spreader in the different contact networks, pre-emptive or immediate vaccination upon identification of the first case is strongly recommended to help better manage the outbreak of mpox and prevent potential straining of healthcare systems.

Exploring the dynamics of the 2022 mpox outbreak in Canada.

The 2022 mpox outbreak predominantly impacted gay, bisexual, and other men who have sex with men (gbMSM). Two models were developed to support situational awareness and management decisions in Canada. A compartmental model characterized epidemic drivers at national/provincial levels, while an agent-based model (ABM) assessed municipal-level impacts of vaccination. The models were parameterized and calibrated using empirical case and vaccination data between 2022 and 2023. The compartmental model explored: (1) the epidemic trajectory through community transmission, (2) the potential for transmission among non-gbMSM, and (3) impacts of vaccination and the proportion of gbMSM contributing to disease transmission. The ABM incorporated sexual-contact data and modeled: (1) effects of vaccine uptake on disease dynamics, and (2) impacts of case importation on outbreak resurgence. The calibrated, compartmental model followed the trajectory of the epidemic, which peaked in July 2022, and died out in December 2022. Most cases occurred among gbMSM, and epidemic trajectories were not consistent with sustained transmission among non-gbMSM. The ABM suggested that unprioritized vaccination strategies could increase the outbreak size by 47%, and that consistent importation (≥5 cases per 10 000) is necessary for outbreak resurgence. These models can inform time-sensitive situational awareness and policy decisions for similar future outbreaks.