Four key challenges in infectious disease modelling using data from multiple sources

Hotspots and Trends in Research on Early Warning of Infectious Diseases: A Bibliometric Analysis Using CiteSpace

Background: Emerging and re-emerging infectious diseases (EIDs and Re-EIDs) cause significant economic crises and public health problems worldwide. Epidemics appear to be more frequent, complex, and harder to prevent. Early warning systems can significantly reduce outbreak response times, contributing to better patient outcomes. Improving early warning systems and methods might be one of the most effective responses. This study employs a bibliometric analysis to dissect the global research hotspots and evolutionary trends in the field of infectious disease early warning, with the aim of providing guidance for optimizing public health emergency management strategies. Methods: Publications related to the role of early warning systems in detecting and responding to infectious disease outbreaks from 1999 to 2024 were retrieved from the Web of Science Core Collection (WoSCC) database. CiteSpace software was used to analyze the datasets and generate knowledge visualization maps. Results: A total of 798 relevant publications are included. The number of annual publications has sharply increased since 2000. The USA produced the highest number of publications and established the most extensive cooperation relationships. The Chinese Center for Disease Control & Prevention was the most productive institution. Drake, John M was the most prolific author, while the World Health Organization and AHMED W were the most cited authors. The top two cited references mainly focused on wastewater surveillance of SARS-CoV-2. The most common keywords were "infectious disease", "outbreak", "transmission", "virus", and "climate change". The basic keyword "climate" ranked the first and long duration with the strongest citation burst. "SARS-CoV-2", "One Health", "early warning system", "artificial intelligence (AI)", and "wastewater-based epidemiology (WBE)" were emerging research foci. Conclusions: Over the past two decades, research on early warning of infectious diseases has focused on climate change, influenza, SARS, virus, machine learning, warning signals and systems, artificial intelligence, and so on. Current research hotspots include wastewater-based epidemiology, sewage, One Health, and artificial intelligence, as well as the early warning and monitoring of COVID-19. Research foci in this area have evolved from focusing on climate-disease interactions to pathogen monitoring systems, and ultimately to the "One Health" integrated framework. Our research findings underscore the imperative for public health policymakers to prioritize investments in real-time surveillance infrastructure, particularly wastewater-based epidemiology and AI-driven predictive models, and strengthen interdisciplinary collaboration frameworks under the One Health paradigm. Developing an integrated human-animal-environment monitoring system will serve as a critical development direction for early warning systems for epidemics.

Research gaps and priorities for quantitative microbial risk assessment (QMRA)

The coronavirus disease 2019 pandemic highlighted the need for more rapid and routine application of modeling approaches such as quantitative microbial risk assessment (QMRA) for protecting public health. QMRA is a transdisciplinary science dedicated to understanding, predicting, and mitigating infectious disease risks. To better equip QMRA researchers to inform policy and public health management, an Advances in Research for QMRA workshop was held to synthesize a path forward for QMRA research. We summarize insights from 41 QMRA researchers and experts to clarify the role of QMRA in risk analysis by (1) identifying key research needs, (2) highlighting emerging applications of QMRA; and (3) describing data needs and key scientific efforts to improve the science of QMRA. Key identified research priorities included using molecular tools in QMRA, advancing dose-response methodology, addressing needed exposure assessments, harmonizing environmental monitoring for QMRA, unifying a divide between disease transmission and QMRA models, calibrating and/or validating QMRA models, modeling co-exposures and mixtures, and standardizing practices for incorporating variability and uncertainty throughout the source-to-outcome continuum. Cross-cutting needs identified were to: develop a community of research and practice, integrate QMRA with other scientific approaches, increase QMRA translation and impacts, build communication strategies, and encourage sustainable funding mechanisms. Ultimately, a vision for advancing the science of QMRA is outlined for informing national to global health assessments, controls, and policies.

Symptom propagation in respiratory pathogens of public health concern: a review of the evidence

Symptom propagation occurs when the symptom set an individual experiences is correlated with the symptom set of the individual who infected them. Symptom propagation may dramatically affect epidemiological outcomes, potentially causing clusters of severe disease. Conversely, it could result in chains of mild infection, generating widespread immunity with minimal cost to public health. Despite accumulating evidence that symptom propagation occurs for many respiratory pathogens, the underlying mechanisms are not well understood. Here, we conducted a scoping literature review for 14 respiratory pathogens to ascertain the extent of evidence for symptom propagation by two mechanisms: dose-severity relationships and route-severity relationships. We identify considerable heterogeneity between pathogens in the relative importance of the two mechanisms, highlighting the importance of pathogen-specific investigations. For almost all pathogens, including influenza and SARS-CoV-2, we found support for at least one of the two mechanisms. For some pathogens, including influenza, we found convincing evidence that both mechanisms contribute to symptom propagation. Furthermore, infectious disease models traditionally do not include symptom propagation. We summarize the present state of modelling advancements to address the methodological gap. We then investigate a simplified disease outbreak scenario, finding that under strong symptom propagation, isolating mildly infected individuals can have negative epidemiological implications.

ePyDGGA: automatic configuration for fitting epidemic curves

Many epidemiological models and algorithms are used to fit the parameters of a given epidemic curve. On many occasions, fitting algorithms are interleaved with the actual epidemic models, which yields combinations of model-parameters that are hard to compare among themselves. Here, we provide a model-agnostic framework for epidemic parameter fitting that can (fairly) compare different epidemic models without jeopardizing the quality of the fitted parameters. Briefly, we have developed a Python framework that expects a Python function (epidemic model) and epidemic data and performs parameter fitting using automatic configuration. Our framework is capable of fitting parameters for any type of epidemic model, as long as it is provided as a Python function (or even in a different programming language). Moreover, we provide the code for different types of models, as well as the implementation of 4 concrete models with data to fit them. Documentation, code and examples can be found at https://ulog.udl.cat/static/doc/epidemic-gga/html/index.html .

Accurately summarizing an outbreak using epidemiological models takes time

Recent outbreaks of Mpox and Ebola, and worrying waves of COVID-19, influenza and respiratory syncytial virus, have all led to a sharp increase in the use of epidemiological models to estimate key epidemiological parameters. The feasibility of this estimation task is known as the practical identifiability (PI) problem. Here, we investigate the PI of eight commonly reported statistics of the classic susceptible-infectious-recovered model using a new measure that shows how much a researcher can expect to learn in a model-based Bayesian analysis of prevalence data. Our findings show that the basic reproductive number and final outbreak size are often poorly identified, with learning exceeding that of individual model parameters only in the early stages of an outbreak. The peak intensity, peak timing and initial growth rate are better identified, being in expectation over 20 times more probable having seen the data by the time the underlying outbreak peaks. We then test PI for a variety of true parameter combinations and find that PI is especially problematic in slow-growing or less-severe outbreaks. These results add to the growing body of literature questioning the reliability of inferences from epidemiological models when limited data are available.

An approximate diffusion process for environmental stochasticity in infectious disease transmission modelling

Modelling the transmission dynamics of an infectious disease is a complex task. Not only it is difficult to accurately model the inherent non-stationarity and heterogeneity of transmission, but it is nearly impossible to describe, mechanistically, changes in extrinsic environmental factors including public behaviour and seasonal fluctuations. An elegant approach to capturing environmental stochasticity is to model the force of infection as a stochastic process. However, inference in this context requires solving a computationally expensive "missing data" problem, using data-augmentation techniques. We propose to model the time-varying transmission-potential as an approximate diffusion process using a path-wise series expansion of Brownian motion. This approximation replaces the "missing data" imputation step with the inference of the expansion coefficients: a simpler and computationally cheaper task. We illustrate the merit of this approach through three examples: modelling influenza using a canonical SIR model, capturing seasonality using a SIRS model, and the modelling of COVID-19 pandemic using a multi-type SEIR model.

A real-time regional model for COVID-19: Probabilistic situational awareness and forecasting

The COVID-19 pandemic is challenging nations with devastating health and economic consequences. The spread of the disease has revealed major geographical heterogeneity because of regionally varying individual behaviour and mobility patterns, unequal meteorological conditions, diverse viral variants, and locally implemented non-pharmaceutical interventions and vaccination roll-out. To support national and regional authorities in surveilling and controlling the pandemic in real-time as it unfolds, we here develop a new regional mathematical and statistical model. The model, which has been in use in Norway during the first two years of the pandemic, is informed by real-time mobility estimates from mobile phone data and laboratory-confirmed case and hospitalisation incidence. To estimate regional and time-varying transmissibility, case detection probabilities, and missed imported cases, we developed a novel sequential Approximate Bayesian Computation method allowing inference in useful time, despite the high parametric dimension. We test our approach on Norway and find that three-week-ahead predictions are precise and well-calibrated, enabling policy-relevant situational awareness at a local scale. By comparing the reproduction numbers before and after lockdowns, we identify spatially heterogeneous patterns in their effect on the transmissibility, with a stronger effect in the most populated regions compared to the national reduction estimated to be 85% (95% CI 78%-89%). Our approach is the first regional changepoint stochastic metapopulation model capable of real time spatially refined surveillance and forecasting during emergencies.

Mechanistic models of Rift Valley fever virus transmission: A systematic review

Rift Valley fever (RVF) is a zoonotic arbovirosis which has been reported across Africa including the northernmost edge, South West Indian Ocean islands, and the Arabian Peninsula. The virus is responsible for high abortion rates and mortality in young ruminants, with economic impacts in affected countries. To date, RVF epidemiological mechanisms are not fully understood, due to the multiplicity of implicated vertebrate hosts, vectors, and ecosystems. In this context, mathematical models are useful tools to develop our understanding of complex systems, and mechanistic models are particularly suited to data-scarce settings. Here, we performed a systematic review of mechanistic models studying RVF, to explore their diversity and their contribution to the understanding of this disease epidemiology. Researching Pubmed and Scopus databases (October 2021), we eventually selected 48 papers, presenting overall 49 different models with numerical application to RVF. We categorized models as theoretical, applied, or grey, depending on whether they represented a specific geographical context or not, and whether they relied on an extensive use of data. We discussed their contributions to the understanding of RVF epidemiology, and highlighted that theoretical and applied models are used differently yet meet common objectives. Through the examination of model features, we identified research questions left unexplored across scales, such as the role of animal mobility, as well as the relative contributions of host and vector species to transmission. Importantly, we noted a substantial lack of justification when choosing a functional form for the force of infection. Overall, we showed a great diversity in RVF models, leading to important progress in our comprehension of epidemiological mechanisms. To go further, data gaps must be filled, and modelers need to improve their code accessibility.

Estimation of age-stratified contact rates during the COVID-19 pandemic using a novel inference algorithm

Well parameterized epidemiological models including accurate representation of contacts are fundamental to controlling epidemics. However, age-stratified contacts are typically estimated from pre-pandemic/peace-time surveys, even though interventions and public response likely alter contacts. Here, we fit age-stratified models, including re-estimation of relative contact rates between age classes, to public data describing the 2020-2021 COVID-19 outbreak in England. This data includes age-stratified population size, cases, deaths, hospital admissions and results from the Coronavirus Infection Survey (almost 9000 observations in all). Fitting stochastic compartmental models to such detailed data is extremely challenging, especially considering the large number of model parameters being estimated (over 150). An efficient new inference algorithm ABC-MBP combining existing approximate Bayesian computation (ABC) methodology with model-based proposals (MBPs) is applied. Modified contact rates are inferred alongside time-varying reproduction numbers that quantify changes in overall transmission due to pandemic response, and age-stratified proportions of asymptomatic cases, hospitalization rates and deaths. These inferences are robust to a range of assumptions including the values of parameters that cannot be estimated from available data. ABC-MBP is shown to enable reliable joint analysis of complex epidemiological data yielding consistent parametrization of dynamic transmission models that can inform data-driven public health policy and interventions. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'.

Quantifying the information in noisy epidemic curves

Reliably estimating the dynamics of transmissible diseases from noisy surveillance data is an enduring problem in modern epidemiology. Key parameters are often inferred from incident time series, with the aim of informing policy-makers on the growth rate of outbreaks or testing hypotheses about the effectiveness of public health interventions. However, the reliability of these inferences depends critically on reporting errors and latencies innate to the time series. Here, we develop an analytical framework to quantify the uncertainty induced by under-reporting and delays in reporting infections, as well as a metric for ranking surveillance data informativeness. We apply this metric to two primary data sources for inferring the instantaneous reproduction number: epidemic case and death curves. We find that the assumption of death curves as more reliable, commonly made for acute infectious diseases such as COVID-19 and influenza, is not obvious and possibly untrue in many settings. Our framework clarifies and quantifies how actionable information about pathogen transmissibility is lost due to surveillance limitations.

Fitting to the UK COVID-19 outbreak, short-term forecasts and estimating the reproductive number

The COVID-19 pandemic has brought to the fore the need for policy makers to receive timely and ongoing scientific guidance in response to this recently emerged human infectious disease. Fitting mathematical models of infectious disease transmission to the available epidemiological data provide a key statistical tool for understanding the many quantities of interest that are not explicit in the underlying epidemiological data streams. Of these, the effective reproduction number, [Formula: see text], has taken on special significance in terms of the general understanding of whether the epidemic is under control ([Formula: see text]). Unfortunately, none of the epidemiological data streams are designed for modelling, hence assimilating information from multiple (often changing) sources of data is a major challenge that is particularly stark in novel disease outbreaks. Here, focusing on the dynamics of the first wave (March-June 2020), we present in some detail the inference scheme employed for calibrating the Warwick COVID-19 model to the available public health data streams, which span hospitalisations, critical care occupancy, mortality and serological testing. We then perform computational simulations, making use of the acquired parameter posterior distributions, to assess how the accuracy of short-term predictions varied over the time course of the outbreak. To conclude, we compare how refinements to data streams and model structure impact estimates of epidemiological measures, including the estimated growth rate and daily incidence.

Interoperability of statistical models in pandemic preparedness: principles and reality

We present interoperability as a guiding framework for statistical modelling to assist policy makers asking multiple questions using diverse datasets in the face of an evolving pandemic response. Interoperability provides an important set of principles for future pandemic preparedness, through the joint design and deployment of adaptable systems of statistical models for disease surveillance using probabilistic reasoning. We illustrate this through case studies for inferring and characterising spatial-temporal prevalence and reproduction numbers of SARS-CoV-2 infections in England.

Challenges in estimation, uncertainty quantification and elicitation for pandemic modelling

The estimation of parameters and model structure for informing infectious disease response has become a focal point of the recent pandemic. However, it has also highlighted a plethora of challenges remaining in the fast and robust extraction of information using data and models to help inform policy. In this paper, we identify and discuss four broad challenges in the estimation paradigm relating to infectious disease modelling, namely the Uncertainty Quantification framework, data challenges in estimation, model-based inference and prediction, and expert judgement. We also postulate priorities in estimation methodology to facilitate preparation for future pandemics.

A computationally tractable birth-death model that combines phylogenetic and epidemiological data

Inferring the dynamics of pathogen transmission during an outbreak is an important problem in infectious disease epidemiology. In mathematical epidemiology, estimates are often informed by time series of confirmed cases, while in phylodynamics genetic sequences of the pathogen, sampled through time, are the primary data source. Each type of data provides different, and potentially complementary, insight. Recent studies have recognised that combining data sources can improve estimates of the transmission rate and the number of infected individuals. However, inference methods are typically highly specialised and field-specific and are either computationally prohibitive or require intensive simulation, limiting their real-time utility. We present a novel birth-death phylogenetic model and derive a tractable analytic approximation of its likelihood, the computational complexity of which is linear in the size of the dataset. This approach combines epidemiological and phylodynamic data to produce estimates of key parameters of transmission dynamics and the unobserved prevalence. Using simulated data, we show (a) that the approximation agrees well with existing methods, (b) validate the claim of linear complexity and (c) explore robustness to model misspecification. This approximation facilitates inference on large datasets, which is increasingly important as large genomic sequence datasets become commonplace.

Modeling and predicting the spread of COVID-19

The world is currently overwhelmed with the perils of the outbreak of the coronavirus disease 2019 (COVID-19) pandemic. As of May 18, 2020, there were 4,819,102 confirmed cases, of which there were 316,959 deaths worldwide. The devastating effects of the COVID-19 pandemic on the world economy are more grievous than many natural disasters like earthquakes and tsunamis in history. Understanding the spread pattern of COVID-19 and predicting the disease dynamics have been essential to assist policymakers and health practitioners in the public and private health sector in providing an efficient way of alleviating the effects of the pandemic across continents. Scholars have steadily worked to provide timely information. Nevertheless, there is a lack of information on which insights can be derived from all these endeavors, especially with regard to modeling and prediction techniques. In this study, we used a literature synthesis approach to provide a narrative review of the current research efforts geared toward predicting the spread of COVID-19 across continents. Such information is useful to provide a global perspective of the virus particularly with regard to modeling and prediction techniques and their outcomes. A total of 69 peer-reviewed articles were reviewed. We found that most articles were from Asia (34.8%) and Europe (23.2%), followed by North America (14.5%), and very few emanated from other continents including Africa and Australia (6.8% each), while no study was reported in Antarctica. Most of the modeling and predictions were based on compartmental epidemiologic models and a few used advanced machine learning techniques. While some models have accurately predicted the end of the epidemic in some countries, other predictions strongly deviate from reality. Interestingly, some studies showed that combining artificial intelligence with classical compartmental models provides a better prediction of the disease spread. Assumptions made when parameterizing the models might be wrong and might not suit the local contexts and might partly explain the observed deviation from the reality on the ground. Furthermore, lack of publicly available key data such as age, gender, comorbidity, and historical medical data of cases and deaths in some continents could limit researchers in addressing some essential aspects of the virus spread and its consequences.

Fitting to the UK COVID-19 outbreak, short-term forecasts and estimating the reproductive number

The COVID-19 pandemic has brought to the fore the need for policy makers to receive timely and ongoing scientific guidance in response to this recently emerged human infectious disease. Fitting mathematical models of infectious disease transmission to the available epidemiological data provides a key statistical tool for understanding the many quantities of interest that are not explicit in the underlying epidemiological data streams. Of these, the effective reproduction number, R, has taken on special significance in terms of the general understanding of whether the epidemic is under control (R < 1). Unfortunately, none of the epidemiological data streams are designed for modelling, hence assimilating information from multiple (often changing) sources of data is a major challenge that is particularly stark in novel disease outbreaks. Here, focusing on the dynamics of the first-wave (March-June 2020), we present in some detail the inference scheme employed for calibrating the Warwick COVID-19 model to the available public health data streams, which span hospitalisations, critical care occupancy, mortality and serological testing. We then perform computational simulations, making use of the acquired parameter posterior distributions, to assess how the accuracy of short-term predictions varied over the timecourse of the outbreak. To conclude, we compare how refinements to data streams and model structure impact estimates of epidemiological measures, including the estimated growth rate and daily incidence.

Modeling Provincial Covid-19 Epidemic Data Using an Adjusted Time-Dependent SIRD Model

In this paper, we develop a forecasting model for the spread of COVID-19 infection at a provincial (i.e., EU NUTS-3) level in Italy by using official data from the Italian Ministry of Health integrated with data extracted from daily official press conferences of regional authorities and local newspaper websites. This data integration is needed as COVID-19 death data are not available at the NUTS-3 level from official open data channels. An adjusted time-dependent SIRD model is used to predict the behavior of the epidemic; specifically, the number of susceptible, infected, deceased, recovered people and epidemiological parameters. Predictive model performance is evaluated using comparison with real data.

Harnessing Social Media in the Modelling of Pandemics-Challenges and Opportunities

As COVID-19 spreads throughout the world without a straightforward treatment or widespread vaccine coverage in the near future, mathematical models of disease spread and of the potential impact of mitigation measures have been thrust into the limelight. With their popularity and ability to disseminate information relatively freely and rapidly, information from social media platforms offers a user-generated, spontaneous insight into users' minds that may capture beliefs, opinions, attitudes, intentions and behaviour towards outbreaks of infectious disease not obtainable elsewhere. The interactive, immersive nature of social media may reveal emergent behaviour that does not occur in engagement with traditional mass media or conventional surveys. In recognition of the dramatic shift to life online during the COVID-19 pandemic to mitigate disease spread and the increasing threat of further pandemics, we examine the challenges and opportunities inherent in the use of social media data in infectious disease modelling with particular focus on their inclusion in compartmental models.

Estimation of COVID-19 epidemic curves using genetic programming algorithm

This paper investigates the possibility of the implementation of Genetic Programming (GP) algorithm on a publicly available COVID-19 data set, in order to obtain mathematical models which could be used for estimation of confirmed, deceased, and recovered cases and the estimation of epidemiology curve for specific countries, with a high number of cases, such as China, Italy, Spain, and USA and as well as on the global scale. The conducted investigation shows that the best mathematical models produced for estimating confirmed and deceased cases achieved R² scores of 0.999, while the models developed for estimation of recovered cases achieved the R² score of 0.998. The equations generated for confirmed, deceased, and recovered cases were combined in order to estimate the epidemiology curve of specific countries and on the global scale. The estimated epidemiology curve for each country obtained from these equations is almost identical to the real data contained within the data set.

Modeling the cost-effectiveness of maternal acellular pertussis immunization (aP) in different socioeconomic settings: A dynamic transmission model of pertussis in three Brazilian states

OBJECTIVES

Using dynamic transmission models we evaluated the health and cost outcomes of adding acellular pertussis (aP) vaccination of pregnant women to infant vaccination in three Brazilian states that represent different socioeconomic conditions. The primary objective was to determine whether the same model structure could be used to represent pertussis disease dynamics in differing socioeconomic conditions.

METHODS

We tested three model structures (SIR, SIRS, SIRSIs) to represent population-level transmission in three socio-demographically distinct Brazilian states: São Paulo, Paraná and Bahia. Two strategies were evaluated: infant wP vaccination alone versus maternal aP immunization plus infant wP vaccination. Model projections for 2014-2029 include outpatient and inpatient pertussis cases, pertussis deaths, years of life lost, disability-adjusted life-years (DALYs) lost, and costs (in 2014 USD) of maternal aP vaccination, infant vaccination, and pertussis medical treatment. Incremental cost per DALY averted is presented from the perspective of the Brazilian National Health System.

RESULTS

Based on goodness-of-fit statistics, the SIRSIs model fit best, although it had only a modest improvement in statistical quantitative assessments relative to the SIRS model. For all three Brazilian states, maternal aP immunization led to higher costs but also saved infant lives and averted DALYs. The 2014 USD cost/DALY averted was $3068 in Sao Paulo, $2962 in Parana, and $2022 in Bahia. These results were robust in sensitivity analyses with the incremental cost-effectiveness ratios exceeding per capita gross regional product only when the probability that a pertussis case is reported was assumed higher than base case implying more overt cases and deaths and therefore more medical costs.

CONCLUSIONS

The same model structure fit all three states best, supporting the idea that the disease behaves similarly across different socioeconomic conditions. We also found that immunization of pregnant women with aP is cost-effective in diverse Brazilian states.

West Nile virus in California, 2003-2018: A persistent threat

The California Arbovirus Surveillance Program was initiated over 50 years ago to track endemic encephalitides and was enhanced in 2000 to include West Nile virus (WNV) infections in humans, mosquitoes, sentinel chickens, dead birds and horses. This comprehensive statewide program is a function of strong partnerships among the California Department of Public Health (CDPH), the University of California, and local vector control and public health agencies. This manuscript summarizes WNV surveillance data in California since WNV was first detected in 2003 in southern California. From 2003 through 2018, 6,909 human cases of WNV disease, inclusive of 326 deaths, were reported to CDPH, as well as 730 asymptomatic WNV infections identified during screening of blood and organ donors. Of these, 4,073 (59.0%) were reported as West Nile neuroinvasive disease. California's WNV disease burden comprised 15% of all cases that were reported to the U.S. Centers for Disease Control and Prevention during this time, more than any other state. Additionally, 1,299 equine WNV cases were identified, along with detections of WNV in 23,322 dead birds, 31,695 mosquito pools, and 7,340 sentinel chickens. Annual enzootic detection of WNV typically preceded detection in humans and prompted enhanced intervention to reduce the risk of WNV transmission. Peak WNV activity occurred from July through October in the Central Valley and southern California. Less than five percent of WNV activity occurred in other regions of the state or outside of this time. WNV continues to be a major threat to public and wild avian health in California, particularly in southern California and the Central Valley during summer and early fall months. Local and state public health partners must continue statewide human and mosquito surveillance and facilitate effective mosquito control and bite prevention measures.

Patch dynamics modeling framework from pathogens' perspective: Unified and standardized approach for complicated epidemic systems

Mathematical models are powerful tools to investigate, simulate, and evaluate potential interventions for infectious diseases dynamics. Much effort has focused on the Susceptible-Infected-Recovered (SIR)-type compartment models. These models consider host populations and measure change of each compartment. In this study, we propose an alternative patch dynamic modeling framework from pathogens' perspective. Each patch, the basic module of this modeling framework, has four standard mechanisms of pathogen population size change: birth (replication), death, inflow, and outflow. This framework naturally distinguishes between-host transmission process (inflow and outflow) and within-host infection process (replication) during the entire transmission-infection cycle. We demonstrate that the SIR-type model is actually a special cross-sectional and discretized case of our patch dynamics model in pathogens' viewpoint. In addition, this patch dynamics modeling framework is also an agent-based model from hosts' perspective by incorporating individual host's specific traits. We provide an operational standard to formulate this modular-designed patch dynamics model. Model parameterization is feasible with a wide range of sources, including genomics data, surveillance data, electronic health record, and from other emerging technologies such as multiomics. We then provide two proof-of-concept case studies to tackle some of the existing challenges of SIR-type models: sexually transmitted disease and healthcare acquired infections. This patch dynamics modeling framework not only provides theoretical explanations to known phenomena, but also generates novel insights of disease dynamics from a more holistic viewpoint. It is also able to simulate and handle more complicated scenarios across biological scales such as the current COVID-19 pandemic.

Estimating Transmission Parameters for Respiratory Syncytial Virus and Predicting the Impact of Maternal and Pediatric Vaccination

BACKGROUND

Respiratory syncytial virus (RSV) is a leading cause of respiratory tract illness in young children and a major cause of hospital admissions globally.

METHODS

Here we fit age-structured transmission models with immunity propagation to data from the Netherlands (2012-2017). Data included nationwide hospitalizations with confirmed RSV, general practitioner (GP) data on attendance for care from acute respiratory infection, and virological testing of acute respiratory infections at the GP. The transmission models, equipped with key parameter estimates, were used to predict the impact of maternal and pediatric vaccination.

RESULTS

Estimates of the basic reproduction number were generally high (R0 > 10 in scenarios with high statistical support), while susceptibility was estimated to be low in nonelderly adults (<10% in persons 20-64 years) and was higher in older adults (≥65 years). Scenario analyses predicted that maternal vaccination reduces the incidence of infection in vulnerable infants (<1 year) and shifts the age of first infection from infants to young children.

CONCLUSIONS

Pediatric vaccination is expected to reduce the incidence of infection in infants and young children (0-5 years), slightly increase incidence in 5 to 9-year-old children, and have minor indirect benefits.

Influencing public health policy with data-informed mathematical models of infectious diseases: Recent developments and new challenges

Modern data and computational resources, coupled with algorithmic and theoretical advances to exploit these, allow disease dynamic models to be parameterised with increasing detail and accuracy. While this enhances models' usefulness in prediction and policy, major challenges remain. In particular, lack of identifiability of a model's parameters may limit the usefulness of the model. While lack of parameter identifiability may be resolved through incorporation into an inference procedure of prior knowledge, formulating such knowledge is often difficult. Furthermore, there are practical challenges associated with acquiring data of sufficient quantity and quality. Here, we discuss recent progress on these issues.

Key questions for modelling COVID-19 exit strategies

Combinations of intense non-pharmaceutical interventions (lockdowns) were introduced worldwide to reduce SARS-CoV-2 transmission. Many governments have begun to implement exit strategies that relax restrictions while attempting to control the risk of a surge in cases. Mathematical modelling has played a central role in guiding interventions, but the challenge of designing optimal exit strategies in the face of ongoing transmission is unprecedented. Here, we report discussions from the Isaac Newton Institute 'Models for an exit strategy' workshop (11-15 May 2020). A diverse community of modellers who are providing evidence to governments worldwide were asked to identify the main questions that, if answered, would allow for more accurate predictions of the effects of different exit strategies. Based on these questions, we propose a roadmap to facilitate the development of reliable models to guide exit strategies. This roadmap requires a global collaborative effort from the scientific community and policymakers, and has three parts: (i) improve estimation of key epidemiological parameters; (ii) understand sources of heterogeneity in populations; and (iii) focus on requirements for data collection, particularly in low-to-middle-income countries. This will provide important information for planning exit strategies that balance socio-economic benefits with public health.

How mechanistic modelling supports decision making for the control of enzootic infectious diseases

Controlling enzootic diseases, which generate a large cumulative burden and are often unregulated, is needed for sustainable farming, competitive agri-food chains, and veterinary public health. We discuss the benefits and challenges of mechanistic epidemiological modelling for livestock enzootics, with particular emphasis on the need for interdisciplinary approaches. We focus on issues arising when modelling pathogen spread at various scales (from farm to the region) to better assess disease control and propose targeted options. We discuss in particular the inclusion of farmers' strategic decision-making, the integration of within-host scale to refine intervention targeting, and the need to ground models on data.

An interim review of the epidemiological characteristics of 2019 novel coronavirus

OBJECTIVES

The 2019 novel coronavirus (2019-nCoV) from Wuhan, China is currently recognized as a public health emergency of global concern.

METHODS

We reviewed the currently available literature to provide up-to-date guidance on control measures to be implemented by public health authorities.

RESULTS

Some of the epidemiological characteristics of 2019-nCoV have been identified. However, there remain considerable uncertainties, which should be considered when providing guidance to public health authorities on control measures.

CONCLUSIONS

Additional studies incorporating more detailed information from confirmed cases would be valuable.

Impact of demographic disparities in social distancing and vaccination on influenza epidemics in urban and rural regions of the United States

Background

Self-protective behaviors of social distancing and vaccination uptake vary by demographics and affect the transmission dynamics of influenza in the United States. By incorporating the socio-behavioral differences in social distancing and vaccination uptake into mathematical models of influenza transmission dynamics, we can improve our estimates of epidemic outcomes. In this study we analyze the impact of demographic disparities in social distancing and vaccination on influenza epidemics in urban and rural regions of the United States.

Methods

We conducted a survey of a nationally representative sample of US adults to collect data on their self-protective behaviors, including social distancing and vaccination to protect themselves from influenza infection. We incorporated this data in an agent-based model to simulate the transmission dynamics of influenza in the urban region of Miami Dade county in Florida and the rural region of Montgomery county in Virginia.

Results

We compare epidemic scenarios wherein the social distancing and vaccination behaviors are uniform versus non-uniform across different demographic subpopulations. We infer that a uniform compliance of social distancing and vaccination uptake among different demographic subpopulations underestimates the severity of the epidemic in comparison to differentiated compliance among different demographic subpopulations. This result holds for both urban and rural regions.

Conclusions

By taking into account the behavioral differences in social distancing and vaccination uptake among different demographic subpopulations in analysis of influenza epidemics, we provide improved estimates of epidemic outcomes that can assist in improved public health interventions for prevention and control of influenza.

Electronic supplementary material

The online version of this article (10.1186/s12879-019-3703-2) contains supplementary material, which is available to authorized users.

Accounting for Healthcare-Seeking Behaviours and Testing Practices in Real-Time Influenza Forecasts

For diseases such as influenza, where the majority of infected persons experience mild (if any) symptoms, surveillance systems are sensitive to changes in healthcare-seeking and clinical decision-making behaviours. This presents a challenge when trying to interpret surveillance data in near-real-time (e.g., to provide public health decision-support). Australia experienced a particularly large and severe influenza season in 2017, perhaps in part due to: (a) mild cases being more likely to seek healthcare; and (b) clinicians being more likely to collect specimens for reverse transcription polymerase chain reaction (RT-PCR) influenza tests. In this study, we used weekly Flutracking surveillance data to estimate the probability that a person with influenza-like illness (ILI) would seek healthcare and have a specimen collected. We then used this estimated probability to calibrate near-real-time seasonal influenza forecasts at each week of the 2017 season, to see whether predictive skill could be improved. While the number of self-reported influenza tests in the weekly surveys are typically very low, we were able to detect a substantial change in healthcare seeking behaviour and clinician testing behaviour prior to the high epidemic peak. Adjusting for these changes in behaviour in the forecasting framework improved predictive skill. Our analysis demonstrates a unique value of community-level surveillance systems, such as Flutracking, when interpreting traditional surveillance data. These methods are also applicable beyond the Australian context, as similar community-level surveillance systems operate in other countries.

An Evidence Synthesis Approach to Estimating the Proportion of Influenza Among Influenza-like Illness Patients

BACKGROUND

Estimation of the national-level incidence of seasonal influenza is notoriously challenging. Surveillance of influenza-like illness is carried out in many countries using a variety of data sources, and several methods have been developed to estimate influenza incidence. Our aim was to obtain maximally informed estimates of the proportion of influenza-like illness that is true influenza using all available data.

METHODS

We combined data on weekly general practice sentinel surveillance consultation rates for influenza-like illness, virologic testing of sampled patients with influenza-like illness, and positive laboratory tests for influenza and other pathogens, applying Bayesian evidence synthesis to estimate the positive predictive value (PPV) of influenza-like illness as a test for influenza virus infection. We estimated the weekly number of influenza-like illness consultations attributable to influenza for nine influenza seasons, and for four age groups.

RESULTS

The estimated PPV for influenza in influenza-like illness patients was highest in the weeks surrounding seasonal peaks in influenza-like illness rates, dropping to near zero in between-peak periods. Overall, 14.1% (95% credible interval [CrI]: 13.5%, 14.8%) of influenza-like illness consultations were attributed to influenza infection; the estimated PPV was 50% (95% CrI: 48%, 53%) for the peak weeks and 5.8% during the summer periods.

CONCLUSIONS

The model quantifies the correspondence between influenza-like illness consultations and influenza at a weekly granularity. Even during peak periods, a substantial proportion of influenza-like illness-61%-was not attributed to influenza. The much lower proportion of influenza outside the peak periods reflects the greater circulation of other respiratory pathogens relative to influenza.

Temporally Varying Relative Risks for Infectious Diseases: Implications for Infectious Disease Control

Risks for disease in some population groups relative to others (relative risks) are usually considered to be consistent over time, although they are often modified by other, nontemporal factors. For infectious diseases, in which overall incidence often varies substantially over time, the patterns of temporal changes in relative risks can inform our understanding of basic epidemiologic questions. For example, recent studies suggest that temporal changes in relative risks of infection over the course of an epidemic cycle can both be used to identify population groups that drive infectious disease outbreaks, and help elucidate differences in the effect of vaccination against infection (that is relevant to transmission control) compared with its effect against disease episodes (that reflects individual protection). Patterns of change in the age groups affected over the course of seasonal outbreaks can provide clues to the types of pathogens that could be responsible for diseases for which an infectious cause is suspected. Changing apparent efficacy of vaccines during trials may provide clues to the vaccine's mode of action and/or indicate risk heterogeneity in the trial population. Declining importance of unusual behavioral risk factors may be a signal of increased local transmission of an infection. We review these developments and the related public health implications.

Evidence Synthesis for Stochastic Epidemic Models

In recent years, the role of epidemic models in informing public health policies has progressively grown. Models have become increasingly realistic and more complex, requiring the use of multiple data sources to estimate all quantities of interest. This review summarises the different types of stochastic epidemic models that use evidence synthesis and highlights current challenges.

Bayesian uncertainty quantification for transmissibility of influenza, norovirus and Ebola using information geometry

Infectious diseases exert a large and in many contexts growing burden on human health, but violate most of the assumptions of classical epidemiological statistics and hence require a mathematically sophisticated approach. Viral shedding data are collected during human studies—either where volunteers are infected with a disease or where existing cases are recruited—in which the levels of live virus produced over time are measured. These have traditionally been difficult to analyse due to strong, complex correlations between parameters. Here, we show how a Bayesian approach to the inverse problem together with modern Markov chain Monte Carlo algorithms based on information geometry can overcome these difficulties and yield insights into the disease dynamics of two of the most prevalent human pathogens—influenza and norovirus—as well as Ebola virus disease.

Publicly available software tools for decision-makers during an emergent epidemic-Systematic evaluation of utility and usability

Epidemics and emerging infectious diseases are becoming an increasing threat to global populations-challenging public health practitioners, decision makers and researchers to plan, prepare, identify and respond to outbreaks in near real-timeframes. The aim of this research is to evaluate the range of public domain and freely available software epidemic modelling tools. Twenty freely utilisable software tools underwent assessment of software usability, utility and key functionalities. Stochastic and agent based tools were found to be highly flexible, adaptable, had high utility and many features, but low usability. Deterministic tools were highly usable with average to good levels of utility.

Inferring epidemiological parameters from phylogenies using regression-ABC: A comparative study

Inferring epidemiological parameters such as the R₀ from time-scaled phylogenies is a timely challenge. Most current approaches rely on likelihood functions, which raise specific issues that range from computing these functions to finding their maxima numerically. Here, we present a new regression-based Approximate Bayesian Computation (ABC) approach, which we base on a large variety of summary statistics intended to capture the information contained in the phylogeny and its corresponding lineage-through-time plot. The regression step involves the Least Absolute Shrinkage and Selection Operator (LASSO) method, which is a robust machine learning technique. It allows us to readily deal with the large number of summary statistics, while avoiding resorting to Markov Chain Monte Carlo (MCMC) techniques. To compare our approach to existing ones, we simulated target trees under a variety of epidemiological models and settings, and inferred parameters of interest using the same priors. We found that, for large phylogenies, the accuracy of our regression-ABC is comparable to that of likelihood-based approaches involving birth-death processes implemented in BEAST2. Our approach even outperformed these when inferring the host population size with a Susceptible-Infected-Removed epidemiological model. It also clearly outperformed a recent kernel-ABC approach when assuming a Susceptible-Infected epidemiological model with two host types. Lastly, by re-analyzing data from the early stages of the recent Ebola epidemic in Sierra Leone, we showed that regression-ABC provides more realistic estimates for the duration parameters (latency and infectiousness) than the likelihood-based method. Overall, ABC based on a large variety of summary statistics and a regression method able to perform variable selection and avoid overfitting is a promising approach to analyze large phylogenies.

Given the rapid evolution of many pathogens, analysing their genomes by means of phylogenies can inform us about how they spread. This is the focus of the field known as “phylodynamics”. Most existing methods inferring epidemiological parameters from virus phylogenies are limited by the difficulty of handling complex likelihood functions, which commonly incorporate latent variables. Here, we use an alternative method known as regression-based Approximate Bayesian Computation (ABC), which circumvents this problem by using simulations and dataset comparisons. Since phylogenies are difficult to compare to one another, we introduce many summary statistics to describe them and take advantage of current machine learning techniques able to perform variable selection. We show that the accuracy we reach is comparable to that of existing methods. This accuracy increases with phylogeny size and can even be higher than that of existing methods for some parameters. Overall, regression-based ABC opens new perspectives to infer epidemiological parameters from large phylogenies.

The natural history of Chlamydia trachomatis infection in women: a multi-parameter evidence synthesis

BACKGROUND AND OBJECTIVES

The evidence base supporting the National Chlamydia Screening Programme, initiated in 2003, has been questioned repeatedly, with little consensus on modelling assumptions, parameter values or evidence sources to be used in cost-effectiveness analyses. The purpose of this project was to assemble all available evidence on the prevalence and incidence of Chlamydia trachomatis (CT) in the UK and its sequelae, pelvic inflammatory disease (PID), ectopic pregnancy (EP) and tubal factor infertility (TFI) to review the evidence base in its entirety, assess its consistency and, if possible, arrive at a coherent set of estimates consistent with all the evidence.

METHODS

Evidence was identified using 'high-yield' strategies. Bayesian Multi-Parameter Evidence Synthesis models were constructed for separate subparts of the clinical and population epidemiology of CT. Where possible, different types of data sources were statistically combined to derive coherent estimates. Where evidence was inconsistent, evidence sources were re-interpreted and new estimates derived on a post-hoc basis.

RESULTS

An internally coherent set of estimates was generated, consistent with a multifaceted evidence base, fertility surveys and routine UK statistics on PID and EP. Among the key findings were that the risk of PID (symptomatic or asymptomatic) following an untreated CT infection is 17.1% [95% credible interval (CrI) 6% to 29%] and the risk of salpingitis is 7.3% (95% CrI 2.2% to 14.0%). In women aged 16-24 years, screened at annual intervals, at best, 61% (95% CrI 55% to 67%) of CT-related PID and 22% (95% CrI 7% to 43%) of all PID could be directly prevented. For women aged 16-44 years, the proportions of PID, EP and TFI that are attributable to CT are estimated to be 20% (95% CrI 6% to 38%), 4.9% (95% CrI 1.2% to 12%) and 29% (95% CrI 9% to 56%), respectively. The prevalence of TFI in the UK in women at the end of their reproductive lives is 1.1%: this is consistent with all PID carrying a relatively high risk of reproductive damage, whether diagnosed or not. Every 1000 CT infections in women aged 16-44 years, on average, gives rise to approximately 171 episodes of PID and 73 of salpingitis, 2.0 EPs and 5.1 women with TFI at age 44 years.

CONCLUSIONS AND RESEARCH RECOMMENDATIONS

The study establishes a set of interpretations of the major studies and study designs, under which a coherent set of estimates can be generated. CT is a significant cause of PID and TFI. CT screening is of benefit to the individual, but detection and treatment of incident infection may be more beneficial. Women with lower abdominal pain need better advice on when to seek early medical attention to avoid risk of reproductive damage. The study provides new insights into the reproductive risks of PID and the role of CT. Further research is required on the proportions of PID, EP and TFI attributable to CT to confirm predictions made in this report, and to improve the precision of key estimates. The cost-effectiveness of screening should be re-evaluated using the findings of this report.

FUNDING

The Medical Research Council grant G0801947.

Retrospective forecasting of the 2010–2014 Melbourne influenza seasons using multiple surveillance systems

Accurate forecasting of seasonal influenza epidemics is of great concern to healthcare providers in temperate climates, since these epidemics vary substantially in their size, timing and duration from year to year, making it a challenge to deliver timely and proportionate responses. Previous studies have shown that Bayesian estimation techniques can accurately predict when an influenza epidemic will peak many weeks in advance, and we have previously tailored these methods for metropolitan Melbourne (Australia) and Google Flu Trends data. Here we extend these methods to clinical observation and laboratory-confirmation data for Melbourne, on the grounds that these data sources provide more accurate characterizations of influenza activity. We show that from each of these data sources we can accurately predict the timing of the epidemic peak 4–6 weeks in advance. We also show that makingsimultaneoususe of multiple surveillance systems to improve forecast skill remains a fundamental challenge. Disparate systems provide complementary characterizations of disease activity, which may or may not be comparable, and it is unclear how a ‘ground truth’ for evaluating forecasts against these multiple characterizations might be defined. These findings are a significant step towards making optimal use of routine surveillance data for outbreak forecasting.

Potential impact of dengue vaccination: Insights from two large-scale phase III trials with a tetravalent dengue vaccine

BACKGROUND

A tetravalent dengue vaccine demonstrated its protective efficacy in two phase III efficacy studies. Results from these studies were used to derive vaccination impact in the five Asian (Indonesia, Malaysia, Philippines, Thailand, Vietnam) and the five Latin American countries (Brazil, Colombia, Honduras, Mexico and Puerto Rico) participating in these trials.

METHODS

Vaccination impact was investigated with an age-structured, host-vector, serotype-specific compartmental model. Parameters related to vaccine efficacy and levels of dengue transmission were estimated using data collected during the phase III efficacy studies. Several vaccination programs, including routine vaccination at different ages with and without large catch-up campaigns, were investigated.

RESULTS

All vaccination programs explored translated into significant reductions in dengue cases at the population level over the first 10years following vaccine introduction and beyond. The most efficient age for vaccination varied according to transmission intensity and 9years was close to the most efficient age across all settings. The combination of routine vaccination and large catch-up campaigns was found to enable a rapid reduction of dengue burden after vaccine introduction.

CONCLUSION

Our analysis suggests that dengue vaccination can significantly reduce the public health impact of dengue in countries where the disease is endemic.

Developing a model to assess community-level risk of oral diseases for planning public dental services in Australia

Background

Poor oral health is a chronic condition that can be extremely costly to manage. In Australia, publicly funded dental services are provided to community members deemed to be eligible—those who are socio-economically disadvantaged or determined to be at higher risk of dental disease. Historically public dental services have nominally been allocated based on the size of the eligible population in a geographic area. This approach has been largely inadequate for reducing disparities in dental disease, primarily because the approach is treatment-focused, and oral health is influenced by a variety of interacting factors. This paper describes the developmental process of a multi-dimensional community-level risk assessment model, to profile a community’s risk of poor oral health.

Methods

A search of the evidence base was conducted to identify robust frameworks for conceptualisation of risk factors and associated performance indicators. Government and other agency websites were also searched to identify publicly available data assets with items relevant to oral diseases. Data quality and analysis considerations were assessed for the use of mixed data sources.

Results

Several frameworks and associated indicator sets (twelve national and eight state-wide data collections with relevant indicators) were identified. Determination of the system inputs for the Model were primarily informed by the World Health Organisation’s (WHO) operational model for an Integrated Oral Health-Chronic Disease Prevention System, and Australia’s National Oral Health Plan 2004–2013. Data quality and access informed the final selection of indicators.

Conclusions

Despite limitations in the quality and regularity of data collections, there are numerous data sources available that provide the required data inputs for community-level risk assessment for oral health. Assessing risk in this way will enhance our ability to deliver appropriate public oral health care services and address the uneven distribution of oral disease across the social gradient.

Forecasting influenza outbreak dynamics in Melbourne from Internet search query surveillance data

BACKGROUND

Accurate forecasting of seasonal influenza epidemics is of great concern to healthcare providers in temperate climates, as these epidemics vary substantially in their size, timing and duration from year to year, making it a challenge to deliver timely and proportionate responses. Previous studies have shown that Bayesian estimation techniques can accurately predict when an influenza epidemic will peak many weeks in advance, using existing surveillance data, but these methods must be tailored both to the target population and to the surveillance system.

OBJECTIVES

Our aim was to evaluate whether forecasts of similar accuracy could be obtained for metropolitan Melbourne (Australia).

METHODS

We used the bootstrap particle filter and a mechanistic infection model to generate epidemic forecasts for metropolitan Melbourne (Australia) from weekly Internet search query surveillance data reported by Google Flu Trends for 2006-14.

RESULTS AND CONCLUSIONS

Optimal observation models were selected from hundreds of candidates using a novel approach that treats forecasts akin to receiver operating characteristic (ROC) curves. We show that the timing of the epidemic peak can be accurately predicted 4-6 weeks in advance, but that the magnitude of the epidemic peak and the overall burden are much harder to predict. We then discuss how the infection and observation models and the filtering process may be refined to improve forecast robustness, thereby improving the utility of these methods for healthcare decision support.

Estimation of parameters related to vaccine efficacy and dengue transmission from two large phase III studies

BACKGROUND

A tetravalent dengue vaccine was shown to be efficacious against symptomatic dengue in two phase III efficacy studies performed in five Asian and five Latin American countries. The objective here was to estimate key parameters of a dengue transmission model using the data collected during these studies.

METHODS

Parameter estimation was based on a Sequential Monte Carlo approach and used a cohort version of the transmission model. Serotype-specific basic reproduction numbers were derived for each country. Parameters related to serotype interactions included duration of cross-protection and level of cross-enhancement characterized by differences in symptomaticity for primary, secondary and post-secondary infections. We tested several vaccine efficacy profiles and simulated the evolution of vaccine efficacy over time for the scenarios providing the best fit to the data.

RESULTS

Two reference scenarios were identified. The first included temporary cross-protection and the second combined cross-protection and cross-enhancement upon wild-type infection and following vaccination. Both scenarios were associated with differences in efficacy by serotype, higher efficacy for pre-exposed subjects and against severe dengue, increase in efficacy with doses for naïve subjects and by a more important waning of vaccine protection for subjects when naïve than when pre-exposed. Over 20 years, the median reduction of dengue risk induced by the direct protection conferred by the vaccine ranged from 24% to 47% according to country for the first scenario and from 34% to 54% for the second.

CONCLUSION

Our study is an important first step in deriving a general framework that combines disease dynamics and mechanisms of vaccine protection that could be used to assess the impact of vaccination at a population level.

Modelling challenges in context: lessons from malaria, HIV, and tuberculosis

Malaria, HIV, and tuberculosis (TB) collectively account for several million deaths each year, with all three ranking among the top ten killers in low-income countries. Despite being caused by very different organisms, malaria, HIV, and TB present a suite of challenges for mathematical modellers that are particularly pronounced in these infections, but represent general problems in infectious disease modelling, and highlight many of the challenges described throughout this issue. Here, we describe some of the unifying challenges that arise in modelling malaria, HIV, and TB, including variation in dynamics within the host, diversity in the pathogen, and heterogeneity in human contact networks and behaviour. Through the lens of these three pathogens, we provide specific examples of the other challenges in this issue and discuss their implications for informing public health efforts.