The use of mathematical modeling studies for evidence synthesis and guideline development: A glossary

Modelling techniques in cholera epidemiology: A systematic and critical review

Diverse modelling techniques in cholera epidemiology have been developed and used to (1) study its transmission dynamics, (2) predict and manage cholera outbreaks, and (3) assess the impact of various control and mitigation measures. In this study, we carry out a critical and systematic review of various approaches used for modelling the dynamics of cholera. Also, we discuss the strengths and weaknesses of each modelling approach. A systematic search of articles was conducted in Google Scholar, PubMed, Science Direct, and Taylor & Francis. Eligible studies were those concerned with the dynamics of cholera excluding studies focused on models for cholera transmission in animals, socio-economic factors, and genetic & molecular related studies. A total of 476 peer-reviewed articles met the inclusion criteria, with about 40% (32%) of the studies carried out in Asia (Africa). About 52%, 21%, and 9%, of the studies, were based on compartmental (e.g., SIRB), statistical (time series and regression), and spatial (spatiotemporal clustering) models, respectively, while the rest of the analysed studies used other modelling approaches such as network, machine learning and artificial intelligence, Bayesian, and agent-based approaches. Cholera modelling studies that incorporate vector/housefly transmission of the pathogen are scarce and a small portion of researchers (3.99%) considers the estimation of key epidemiological parameters. Vaccination only platform was utilized as a control measure in more than half (58%) of the studies. Research productivity in cholera epidemiological modelling studies have increased in recent years, but authors used diverse range of models. Future models should consider incorporating vector/housefly transmission of the pathogen and on the estimation of key epidemiological parameters for the transmission of cholera dynamics.

A competency framework on simulation modelling-supported decision-making for Master of Public Health graduates

BACKGROUND

Simulation models are increasingly important for supporting decision-making in public health. However, due to lack of training, many public health professionals remain unfamiliar with constructing simulation models and using their outputs for decision-making. This study contributes to filling this gap by developing a competency framework on simulation model-supported decision-making targeting Master of Public Health education.

METHODS

The study combined a literature review, a two-stage online Delphi survey and an online consensus workshop. A draft competency framework was developed based on 28 peer-reviewed publications. A two-stage online Delphi survey involving 15 experts was conducted to refine the framework. Finally, an online consensus workshop, including six experts, evaluated the competency framework and discussed its implementation.

RESULTS

The competency framework identified 20 competencies related to stakeholder engagement, problem definition, evidence identification, participatory system mapping, model creation and calibration and the interpretation and dissemination of model results. The expert evaluation recommended differentiating professional profiles and levels of expertise and synergizing with existing course contents to support its implementation.

CONCLUSIONS

The competency framework developed in this study is instrumental to including simulation model-supported decision-making in public health training. Future research is required to differentiate expertise levels and develop implementation strategies.

Integrating meta-analysis with a quantitative microbial risk assessment model to investigate Campylobacter contamination of broiler carcasses

This study investigated the prevalence and associated risk factors of Campylobacter in South Korean broilers using a random-effects meta-analysis. Subsequently, to facilitate the design of preventive measures, the prevalence estimate from the meta-analysis was incorporated into a stochastic risk assessment model to quantify the Campylobacter contamination levels on broiler carcasses. The baseline model was developed based on the most common practices along the South Korean broiler processing line, with no interventions. Meta-analysis results revealed Campylobacter prevalence across the chicken supply chain in the following order: farms (60.6 % [57.3-63.4]), retail markets (43.90 % [24.81-64.99]), slaughterhouses (27.71 % [18.56-39.21]), and processing plants (14.50 % [3.96-41.09]). The model estimated a 52 % (36.1-70.8) Campylobacter prevalence at the end of chilling, with an average contamination level of 4.62 (2.50-6.74) log CFU/carcass. Sensitivity analysis indicated that Campylobacter fecal shedding (r = 0.95) and the amount of feces on bird exteriors (r = 0.17) at pre-harvest were the main factors for carcass contamination, while soft scalding (r = -0.22) and air chilling (r = -0.12) can serve as critical control points (CCPs) at harvest. Scenario analysis indicated that a combination of hard scalding, inside-outside bird washing, spray washing, and chlorinated water immersion chilling can offer a 30.9 % reduction in prevalence and a reduction of 2.23 log CFU/carcass in contamination levels compared to the baseline model. Apart from disinfection and sanitation interventions carried out during meat processing, the implementation of robust control measures is indispensable to mitigate Campylobacter prevalence and concentration at broiler farms, thereby enhancing meat safety and public health. Furthermore, given the high Campylobacter prevalence in the retail markets, future studies should explore the potential risk of cross-contamination at post-harvest stage.

DElaying Disease Progression In COPD with Early Initiation of Dual Bronchodilator or Triple Inhaled PharmacoTherapy (DEPICT): A Predictive Modelling Approach

INTRODUCTION

Clinical studies demonstrate an accelerated decline in lung function in patients with moderate chronic obstructive pulmonary disease (COPD) (Global Initiative for Chronic Obstructive Lung Disease [GOLD] grade 2) versus severe and very severe COPD (GOLD grades 3 and 4). This predictive modelling study assessed the impact of initiating pharmacotherapy earlier versus later on long-term disease progression in COPD.

METHODS

The modelling approach used data on decline in forced expiratory volume in 1 s (FEV1) extracted from published studies to develop a longitudinal non-parametric superposition model of lung function decline with progressive impact of exacerbations from 0 per year to 3 per year and no ongoing pharmacotherapy. The model simulated decline in FEV1 and annual exacerbation rates from age 40 to 75 years in COPD with initiation of long-acting anti-muscarinic antagonist (LAMA)/long-acting beta2-agonist (LABA) (umeclidinium (UMEC)/vilanterol (VI)) or triple (inhaled corticosteroid (ICS)/LAMA/LABA; fluticasone furoate (FF)/UMEC/VI) therapy at 40, 55 or 65 years of age.

RESULTS

Model-predicted decline in FEV1 showed that, compared with 'no ongoing' therapy, initiation of triple or LAMA/LABA therapy at age 40, 55 or 65 years preserved an additional 469.7 mL or 236.0 mL, 327.5 mL or 203.3 mL, or 213.5 mL or 137.5 mL of lung function, respectively, by the age of 75. The corresponding average annual exacerbation rates were reduced from 1.57 to 0.91, 1.06 or 1.23 with triple therapy or to 1.2, 1.26 and 1.4 with LAMA/LABA therapy when initiated at 40, 55 or 65 years of age, respectively.

CONCLUSIONS

This modelling study suggests that earlier initiation of LAMA/LABA or triple therapy may have positive benefits in slowing disease progression in patients with COPD. Greater benefits were demonstrated with early initiation therapy with triple versus LAMA/LABA.

Different Trajectories for Diabetes Mellitus Onset and Recovery According to the Centralized Aerobic-Anaerobic Energy Balance Compensation Theory

We recently reported that the restoration of cervical vertebral arterial blood flow access (measured as systolic peak (PS)) to the rhomboid fossa leads to the recovery of the HbA1c level in the case of patients with a pre-Diabetes Mellitus (pre-DM) condition. The theory of centralized aerobic-anaerobic energy balance compensation (TCAAEBC) provides a successful theoretical explanation for this observation. It considers the human body as a dissipative structure. Reported connections between arterial hypertension (AHT) and the level of HbA1c are linked through OABFRH. According to the TCAAEBC, this delivers incorrect information about blood oxygen availability to the cerebellum. The restoration of PS normalizes AHT in 5-6 weeks and HbA1c in 12-13 weeks. In the current study, we demonstrate the model which fits the obtained experimental data. According to the model, pathways of onset and recovery from pre-DM are different. The consequence of these differences is discussed. The great significance of the TCAAEBC for medical practice forces the creation of an appropriate mathematical model, but the required adjustment of the model needs experimental data which can only be obtained from an animal model(s). The essential part of this study is devoted to the analysis of the advantages and disadvantages of widely available common mammalian models for TCAAEBC cases.

Within-host modeling to measure dynamics of antibody responses after natural infection or vaccination: A systematic review

BACKGROUND

Within-host models describe the dynamics of immune cells when encountering a pathogen, and how these dynamics can lead to an individual-specific immune response. This systematic review aims to summarize which within-host methodology has been used to study and quantify antibody kinetics after infection or vaccination. In particular, we focus on data-driven and theory-driven mechanistic models.

MATERIALS

PubMed and Web of Science databases were used to identify eligible papers published until May 2022. Eligible publications included those studying mathematical models that measure antibody kinetics as the primary outcome (ranging from phenomenological to mechanistic models).

RESULTS

We identified 78 eligible publications, of which 8 relied on an Ordinary Differential Equations (ODEs)-based modelling approach to describe antibody kinetics after vaccination, and 12 studies used such models in the context of humoral immunity induced by natural infection. Mechanistic modeling studies were summarized in terms of type of study, sample size, measurements collected, antibody half-life, compartments and parameters included, inferential or analytical method, and model selection.

CONCLUSIONS

Despite the importance of investigating antibody kinetics and underlying mechanisms of (waning of) the humoral immunity, few publications explicitly account for this in a mathematical model. In particular, most research focuses on phenomenological rather than mechanistic models. The limited information on the age groups or other risk factors that might impact antibody kinetics, as well as a lack of experimental or observational data remain important concerns regarding the interpretation of mathematical modeling results. We reviewed the similarities between the kinetics following vaccination and infection, emphasising that it may be worth translating some features from one setting to another. However, we also stress that some biological mechanisms need to be distinguished. We found that data-driven mechanistic models tend to be more simplistic, and theory-driven approaches lack representative data to validate model results.

The Relationship Between Telomeres, Cognition, Mood, and Physical Function: A Systematic Review

Background and Purpose: Cognitive, affective, and physical symptoms and alterations in their function are seen across chronic illnesses. Data suggest that environmental, psychological, and physiological factors contribute to symptom experience, potentially through loss of telomeres (telomere attrition), structures at the ends of chromosomes. Telomere length is affected by many factors including environmental (e.g., exercise, diet, smoking) and physiological (e.g., response to stress), as well as from oxidative damage and inflammation that occurs in many disease processes. Moreover, telomere attrition is associated with chronic disease (cancer, cardiovascular disease, Alzheimer's disease) and predicts higher morbidity and mortality rates. However, findings are inconsistent among telomere roles and relationships with health outcomes. This article aims to synthesize the current state-of-the-science of telomeres and their relationship with cognitive, affective, and physical function and symptoms. Method: A comprehensive literature search was performed in two databases: CINAHL and PUBMED. A total of 33 articles published between 2000 and 2022 were included in the final analysis. Results: Telomere attrition is associated with various changes in cognitive, affective, and physical function and symptoms. However, findings are inconsistent. Interventional studies (e.g., meditation and exercise) may affect telomere attrition, potentially impacting health outcomes. Conclusion: Nursing research and practice are at the forefront of furthering the understanding of telomeres and their relationships with cognitive, affective, and physical function and symptoms. Future interventions targeting modifiable risk factors may be developed to improve health outcomes across populations.

A mathematical model for transmission dynamics of COVID-19 infection

In this paper, a mathematical model of COVID-19 has been proposed to study the transmission dynamics of infection by taking into account the role of symptomatic and asymptomatic infected individuals. The model has also considered the effect of non-pharmaceutical interventions (NPIs) in controlling the spread of virus. The basic reproduction number ( R 0 ) has been computed and the analysis shows that for R 0 < 1 , the disease-free state becomes globally stable. The conditions of existence and stability for two other equilibrium states have been obtained. Transcritical bifurcation occurs when basic reproduction number is one (i.e. R 0 = 1 ). It is found that when asymptomatic cases get increased, infection will persist in the population. However, when symptomatic cases get increased as compared to asymptomatic ones, the endemic state will become unstable and infection may eradicate from the population. Increasing NPIs decrease the basic reproduction number and hence, the epidemic can be controlled. As the COVID-19 transmission is subject to environmental fluctuations, the effect of white noise has been considered in the deterministic model. The stochastic differential equation model has been solved numerically by using the Euler-Maruyama method. The stochastic model gives large fluctuations around the respective deterministic solutions. The model has been fitted by using the COVID-19 data of three waves of India. A good match is obtained between the actual data and the predicted trajectories of the model in all three waves of COVID-19. The findings of this model may assist policymakers and healthcare professionals in implementing the most effective measures to prevent the transmission of COVID-19 in different settings.

The role of modeling studies in asthma management and clinical decision-making: a Delphi survey of physician knowledge and perceptions

OBJECTIVE

To investigate the knowledge and perceptions of physicians on the role of modeling studies in asthma, using a modified Delphi procedure.

METHODS

Group opinions among a panel of respiratory experts were obtained using two online questionnaires and a virtual scientific workshop. A consensus was pre-defined as agreement by >75% of participants.

RESULTS

From 26 experts who agreed to participate, 22 completed both surveys. At the end of the process, the panel rated their own understanding of modeling as good (77%) but that among physicians in general as poor (77%). Participants agreed that data from modeling studies should be used, at least sometimes, to inform treatment guidelines (91%) and could be useful for guiding clinical decisions (100%). Perceived barriers to using modeling studies were 'A lack of understanding' (81%) and 'A lack of standardized methodology' (82%). Based on data from two modeling studies, no consensus was reached on physicians recommending regular inhaled corticosteroids (ICS) versus as-needed therapy for patients with mild asthma, whereas 77% agreed that they would recommend regular ICS over maintenance and reliever therapy for ≥80% of their patients with moderate asthma. No consensus was reached on the value of modeling data in relation to empirical data.

CONCLUSION

There is overall support among respiratory experts for the usefulness of modeling data to guide asthma treatment guidelines and clinical decision making. More publications on modeling data using robust models and accessible terminology will aid the understanding of physicians in general and help clarify the evidence-based value of modeling studies.

Is It Possible to Predict COVID-19? Stochastic System Dynamic Model of Infection Spread in Kazakhstan

BACKGROUND

Since the start of the COVID-19 pandemic, scientists have begun to actively use models to determine the epidemiological characteristics of the pathogen. The transmission rate, recovery rate and loss of immunity to the COVID-19 virus change over time and depend on many factors, such as the seasonality of pneumonia, mobility, testing frequency, the use of masks, the weather, social behavior, stress, public health measures, etc. Therefore, the aim of our study was to predict COVID-19 using a stochastic model based on the system dynamics approach.

METHOD

We developed a modified SIR model in AnyLogic software. The key stochastic component of the model is the transmission rate, which we consider as an implementation of Gaussian random walks with unknown variance, which was learned from real data.

RESULTS

The real data of total cases turned out to be outside the predicted minimum-maximum interval. The minimum predicted values of total cases were closest to the real data. Thus, the stochastic model we propose gives satisfactory results for predicting COVID-19 from 25 to 100 days. The information we currently have about this infection does not allow us to make predictions with high accuracy in the medium and long term.

CONCLUSIONS

In our opinion, the problem of the long-term forecasting of COVID-19 is associated with the absence of any educated guess regarding the dynamics of β(t) in the future. The proposed model requires improvement with the elimination of limitations and the inclusion of more stochastic parameters.

Computational decision-support tools for urban design to improve resilience against COVID-19 and other infectious diseases: A systematic review

The COVID-19 pandemic highlighted the need for decision-support tools to help cities become more resilient to infectious diseases. Through urban design and planning, non-pharmaceutical interventions can be enabled, impelling behaviour change and facilitating the construction of lower risk buildings and public spaces. Computational tools, including computer simulation, statistical models, and artificial intelligence, have been used to support responses to the current pandemic as well as to the spread of previous infectious diseases. Our multidisciplinary research group systematically reviewed state-of-the-art literature to propose a toolkit that employs computational modelling for various interventions and urban design processes. We selected 109 out of 8,737 studies retrieved from databases and analysed them based on the pathogen type, transmission mode and phase, design intervention and process, as well as modelling methodology (method, goal, motivation, focus, and indication to urban design). We also explored the relationship between infectious disease and urban design, as well as computational modelling support, including specific models and parameters. The proposed toolkit will help designers, planners, and computer modellers to select relevant approaches for evaluating design decisions depending on the target disease, geographic context, design stages, and spatial and temporal scales. The findings herein can be regarded as stand-alone tools, particularly for fighting against COVID-19, or be incorporated into broader frameworks to help cities become more resilient to future disasters.

Infectious disease modelling for SARS-CoV-2 in Africa to guide policy: A systematic review

Applied epidemiological models have played a critical role in understanding the transmission and control of disease outbreaks. Their utility and accuracy in decision-making on appropriate responses during public health emergencies is however a factor of their calibration to local data, evidence informing model assumptions, speed of obtaining and communicating their results, ease of understanding and willingness by policymakers to use their insights. We conducted a systematic review of infectious disease models focused on SARS-CoV-2 in Africa to determine: a) spatial and temporal patterns of SARS-CoV-2 modelling in Africa, b) use of local data to calibrate the models and local expertise in modelling activities, and c) key modelling questions and policy insights. We searched PubMed, Embase, Web of Science and MedRxiv databases following the PRISMA guidelines to obtain all SARS-CoV-2 dynamic modelling papers for one or multiple African countries. We extracted data on countries studied, authors and their affiliations, modelling questions addressed, type of models used, use of local data to calibrate the models, and model insights for guiding policy decisions. A total of 74 papers met the inclusion criteria, with nearly two-thirds of these coming from 6% (3) of the African countries. Initial papers were published 2 months after the first cases were reported in Africa, with most papers published after the first wave. More than half of all papers (53, 78%) and (48, 65%) had a first and last author affiliated to an African institution respectively, and only 12% (9) used local data for model calibration. A total of 60% (46) of the papers modelled assessment of control interventions. The transmission rate parameter was found to drive the most uncertainty in the sensitivity analysis for majority of the models. The use of dynamic models to draw policy insights was crucial and therefore there is need to increase modelling capacity in the continent.

Contribution and quality of mathematical modeling evidence in World Health Organization guidelines: A systematic review

Mathematical modeling studies are frequently conducted to guide policy in global health. However, the contribution of mathematical modeling studies to World Health Organization (WHO) guideline recommendations, and the quality of evidence contributed by these studies remains unknown. We conducted a systematic review of the WHO Guidelines Review Committee database to identify guideline recommendations that included evidence from mathematical modeling studies since inception of the Guidelines Review Committee on 1 December, 2007. We included WHO guideline recommendations citing a mathematical modeling study in the primary evidence base. We defined a mathematical model as a framework that predicted epidemiologic, health or economic impact of an intervention or decision in the clinical or public health context. The primary outcome was inclusion of evidence from mathematical modeling studies in a guideline recommendation. We evaluated each unique modeling study across multiple domains of quality. Between 1 December 2007 and 1 April 2019, the WHO Guidelines Review Committee approved 154 guidelines providing 1619 guideline recommendations. Mathematical modeling studies informed 46 WHO guidelines (29.9%) and 101 unique guideline recommendations (6.2%). Modeling evidence addressed topics related to infectious diseases in 38 guidelines (82.6%) and 81 recommendations (80.2%), most commonly for HIV and tuberculosis. Evidence from modeling studies was assessed in the GRADE evidence profile for 12 recommendations (12.9%) and GRADE evidence-to-decision framework for 45 recommendations (44.6%). Modeling-informed recommendations were more likely than other recommendations within the same guidelines to be issued with a "conditional" rather than "strong" strength of recommendation (53.5% versus 37.8%), and the evidence underlying modeling-informed recommendations was more likely to be assessed as very low quality (41.6% versus 24.1%). Upon review of individual modeling studies, we estimated that 33.8% of models performed a calibration, 29.4% of models performed a validation of results, and 20.6% of models reported a change in the study conclusion in the sensitivity analysis. While policy recommendations in WHO guidelines are informed by evidence from modeling studies, the validity of modeling studies included in guidelines development is heterogeneous. Quality assessment is needed to support the evaluation and incorporation of evidence from mathematical modeling studies in guidelines development.

Mathematical Modeling Research Output Impacting New Technological Development: An Axiomatization to Build Novelty

The mathematical modeling of research-based output impacting new technology development is crucial for a developing country. However, the complexity of modern mathematical modeling research output makes it unclear over how it can impact the development of new technology. Therefore, this study aims to explore, categorize and formulize the axioms of mathematical modeling research output that impacts the development of new technology. Seven participants were involved in this research. Interviews were conducted to explore their remarkable mathematical modeling output and how the output can impact the development of new technology. The categorization axioms are: i. mathematical modeling for theorizing, ii. mathematical modeling for simulations, iii. mathematical modeling for useable innovation and iv. patent and product commercialization. Finally, the categorization can be formulized as an axiom of mathematical modeling novelty, which is the desired research output. Moreover, patents and commercialization are the elements that mathematical modeling should possess for new technological development. The limited number of participants involved in this study makes this study formulation limited to only some types of mathematical modeling output. However, this substantive formulation could give some ideas in proposing the path and processes on how to enhance the effort for society to develop the culture of mathematical modeling in developing new technology.

Mechanisms and Methods to Understand Depressive Symptoms

Depressive symptoms, feelings of sadness, anger, and loss that interfere with a person's daily life, are prevalent health concerns across populations that significantly result in adverse health outcomes with direct and indirect economic burdens at a national and global level. This article aims to synthesize known mechanisms of depressive symptoms and the established and emerging methodologies used to understand depressive symptoms; implications and directions for future nursing research are discussed. A comprehensive search was performed by Cumulative Index to Nursing and Allied Health Literature, MEDLINE, and PUBMED databases between 2000-2021 to examine contributing factors of depressive symptoms. Many environmental, psychological, and physiological factors are associated with the development or increased severity of depressive symptoms (anhedonia, fatigue, sleep and appetite disturbances to depressed mood). This paper discusses biological and psychological theories that guide our understanding of depressive symptoms, as well as known biomarkers (gut microbiome, specific genes, multi-cytokine, and hormones) and established and emerging methods. Disruptions within the nervous system, hormonal and neurotransmitters levels, brain structure, gut-brain axis, leaky-gut syndrome, immune and inflammatory process, and genetic variations are significant mediating mechanisms in depressive symptomology. Nursing research and practice are at the forefront of furthering depressive symptoms' mechanisms and methods. Utilizing advanced technology and measurement tools (big data, machine learning/artificial intelligence, and multi-omic approaches) can provide insight into the psychological and biological mechanisms leading to effective intervention development. Thus, understanding depressive symptomology provides a pathway to improve patients' health outcomes, leading to reduced morbidity and mortality and the overall nation-wide economic burden.Supplemental data for this article is available online at https://doi.org/10.1080/01612840.2021.1998261 .

Short-Term and Long-Term COVID-19 Pandemic Forecasting Revisited with the Emergence of OMICRON Variant in Jordan

Three simple approaches to forecast the COVID-19 epidemic in Jordan were previously proposed by Hussein, et al.: a short-term forecast (STF) based on a linear forecast model with a learning database on the reported cases in the previous 5–40 days, a long-term forecast (LTF) based on a mathematical formula that describes the COVID-19 pandemic situation, and a hybrid forecast (HF), which merges the STF and the LTF models. With the emergence of the OMICRON variant, the LTF failed to forecast the pandemic due to vital reasons related to the infection rate and the speed of the OMICRON variant, which is faster than the previous variants. However, the STF remained suitable for the sudden changes in epi curves because these simple models learn for the previous data of reported cases. In this study, we revisited these models by introducing a simple modification for the LTF and the HF model in order to better forecast the COVID-19 pandemic by considering the OMICRON variant. As another approach, we also tested a time-delay neural network (TDNN) to model the dataset. Interestingly, the new modification was to reuse the same function previously used in the LTF model after changing some parameters related to shift and time-lag. Surprisingly, the mathematical function type was still valid, suggesting this is the best one to be used for such pandemic situations of the same virus family. The TDNN was data-driven, and it was robust and successful in capturing the sudden change in +qPCR cases before and after of emergence of the OMICRON variant.

Investigating the impact of vaccination and non-pharmaceutical measures in curbing COVID-19 spread: A South Africa perspective

The year 2020 brought about a pandemic that caught most of the world population by surprise and wreaked unimaginable havoc before any form of effective reaction could be put in place. COVID-19 is proving to be an epidemic that keeps on having an upsurge whenever it looks like it is being curbed. This pandemic has led to continuous strategizing on approaches to quelling the surge. The recent and welcome introduction of vaccines has led to renewed optimism for the population at large. The introduction of vaccines has led to the need to investigate the effect of vaccination among other control measures in the fight against COVID-19. In this study, we develop a mathematical model that captures the dynamics of the disease taking into consideration some measures that are easier to implement majorly within the African context. We consider quarantine and vaccination as control measures and investigate the efficacy of these measures in curbing the reproduction rate of the disease. We analyze the local stability of the disease-free equilibrium point. We also perform sensitivity analysis of the effective reproduction number to determine which parameters significantly lowers the effective reproduction number. The results obtained suggest that quarantine and a vaccine with at least 75% efficacy and reducing transmission probability through sanitation and wearing of protective gears can significantly reduce the number of secondary infections.

Recommended reporting items for epidemic forecasting and prediction research: The EPIFORGE 2020 guidelines

BACKGROUND

The importance of infectious disease epidemic forecasting and prediction research is underscored by decades of communicable disease outbreaks, including COVID-19. Unlike other fields of medical research, such as clinical trials and systematic reviews, no reporting guidelines exist for reporting epidemic forecasting and prediction research despite their utility. We therefore developed the EPIFORGE checklist, a guideline for standardized reporting of epidemic forecasting research.

METHODS AND FINDINGS

We developed this checklist using a best-practice process for development of reporting guidelines, involving a Delphi process and broad consultation with an international panel of infectious disease modelers and model end users. The objectives of these guidelines are to improve the consistency, reproducibility, comparability, and quality of epidemic forecasting reporting. The guidelines are not designed to advise scientists on how to perform epidemic forecasting and prediction research, but rather to serve as a standard for reporting critical methodological details of such studies.

CONCLUSIONS

These guidelines have been submitted to the EQUATOR network, in addition to hosting by other dedicated webpages to facilitate feedback and journal endorsement.

How are mathematical models and results from mathematical models of vaccine-preventable diseases used, or not, by global health organisations?

While epidemiological and economic evidence has the potential to provide answers to questions, guide complex programmes and inform resource allocation decisions, how this evidence is used by global health organisations who commission it and what organisational actions are generated from the evidence remains unclear. This study applies analytical tools from organisational science to understand how evidence produced by infectious disease epidemiologists and health economists is used by global health organisations. A conceptual framework that embraces evidence use typologies and relates findings to the organisational process of action generation informs and structures the research. Between March and September 2020, we conducted in-depth interviews with mathematical modellers (evidence producers) and employees at global health organisations, who are involved in decision-making processes (evidence consumers). We found that commissioned epidemiological and economic evidence is used to track progress and provides a measure of success, both in terms of health outcomes and the organisations’ mission. Global health organisations predominantly use this evidence to demonstrate accountability and solicit funding from external partners. We find common understanding and awareness across consumers and producers about the purposes and uses of these commissioned pieces of work and how they are distinct from more academic explorative research outputs. Conceptual evidence use best describes this process. Evidence is slowly integrated into organisational processes and is one of many influences on global health organisations’ actions. Relationships developed over time and trust guide the process, which may lead to quite a concentrated cluster of those producing and commissioning models. These findings raise several insights relevant to the literature of research utilisation in organisations and evidence-based management. The study extends our understanding of how evidence is used and which organisational actions are generated as a result of commissioning epidemiological and economic evidence.

PopART-IBM, a highly efficient stochastic individual-based simulation model of generalised HIV epidemics developed in the context of the HPTN 071 (PopART) trial

Mathematical models are powerful tools in HIV epidemiology, producing quantitative projections of key indicators such as HIV incidence and prevalence. In order to improve the accuracy of predictions, such models need to incorporate a number of behavioural and biological heterogeneities, especially those related to the sexual network within which HIV transmission occurs. An individual-based model, which explicitly models sexual partnerships, is thus often the most natural type of model to choose. In this paper we present PopART-IBM, a computationally efficient individual-based model capable of simulating 50 years of an HIV epidemic in a large, high-prevalence community in under a minute. We show how the model calibrates within a Bayesian inference framework to detailed age- and sex-stratified data from multiple sources on HIV prevalence, awareness of HIV status, ART status, and viral suppression for an HPTN 071 (PopART) study community in Zambia, and present future projections of HIV prevalence and incidence for this community in the absence of trial intervention.

In this paper we present PopART-IBM, an individual-based model used to simulate HIV transmission in communities in high prevalence settings. We show that PopART-IBM can simulate transmission over a span of decades in a large community in less than a minute. This computational efficiency allows us to calibrate the model within an inference framework, and we show an illustrative example of calibration using an adaptive population Monte Carlo Approximate Bayesian Computation algorithm for a community in Zambia that was part of the HPTN-071 (PopART) trial. We compare the detailed model output to real-world data collected during the trial from this community. Finally, we project how the HIV epidemic would have changed over time in this community if no intervention from the trial had occurred.

COVID-19 Pandemic Development in Jordan-Short-Term and Long-Term Forecasting

In this study, we proposed three simple approaches to forecast COVID-19 reported cases in a Middle Eastern society (Jordan). The first approach was a short-term forecast (STF) model based on a linear forecast model using the previous days as a learning data-base for forecasting. The second approach was a long-term forecast (LTF) model based on a mathematical formula that best described the current pandemic situation in Jordan. Both approaches can be seen as complementary: the STF can cope with sudden daily changes in the pandemic whereas the LTF can be utilized to predict the upcoming waves' occurrence and strength. As such, the third approach was a hybrid forecast (HF) model merging both the STF and the LTF models. The HF was shown to be an efficient forecast model with excellent accuracy. It is evident that the decision to enforce the curfew at an early stage followed by the planned lockdown has been effective in eliminating a serious wave in April 2020. Vaccination has been effective in combating COVID-19 by reducing infection rates. Based on the forecasting results, there is some possibility that Jordan may face a third wave of the pandemic during the Summer of 2021.

Beyond Behaviour: How Health Inequality Theory Can Enhance Our Understanding of the 'Alcohol-Harm Paradox'

There are large socioeconomic inequalities in alcohol-related harm. The alcohol harm paradox (AHP) is the consistent finding that lower socioeconomic groups consume the same or less as higher socioeconomic groups yet experience greater rates of harm. To date, alcohol researchers have predominantly taken an individualised behavioural approach to understand the AHP. This paper calls for a new approach which draws on theories of health inequality, specifically the social determinants of health, fundamental cause theory, political economy of health and eco-social models. These theories consist of several interwoven causal mechanisms, including genetic inheritance, the role of social networks, the unequal availability of wealth and other resources, the psychosocial experience of lower socioeconomic position, and the accumulation of these experiences over time. To date, research exploring the causes of the AHP has often lacked clear theoretical underpinning. Drawing on these theoretical approaches in alcohol research would not only address this gap but would also result in a structured effort to identify the causes of the AHP. Given the present lack of clear evidence in favour of any specific theory, it is difficult to conclude whether one theory should take primacy in future research efforts. However, drawing on any of these theories would shift how we think about the causes of the paradox, from health behaviour in isolation to the wider context of complex interacting mechanisms between individuals and their environment. Meanwhile, computer simulations have the potential to test the competing theoretical perspectives, both in the abstract and empirically via synthesis of the disparate existing evidence base. Overall, making greater use of existing theoretical frameworks in alcohol epidemiology would offer novel insights into the AHP and generate knowledge of how to intervene to mitigate inequalities in alcohol-related harm.

Prediction Models for Public Health Containment Measures on COVID-19 Using Artificial Intelligence and Machine Learning: A Systematic Review

Artificial Intelligence (AI) and Machine Learning (ML) have expanded their utilization in different fields of medicine. During the SARS-CoV-2 outbreak, AI and ML were also applied for the evaluation and/or implementation of public health interventions aimed to flatten the epidemiological curve. This systematic review aims to evaluate the effectiveness of the use of AI and ML when applied to public health interventions to contain the spread of SARS-CoV-2. Our findings showed that quarantine should be the best strategy for containing COVID-19. Nationwide lockdown also showed positive impact, whereas social distancing should be considered to be effective only in combination with other interventions including the closure of schools and commercial activities and the limitation of public transportation. Our findings also showed that all the interventions should be initiated early in the pandemic and continued for a sustained period. Despite the study limitation, we concluded that AI and ML could be of help for policy makers to define the strategies for containing the COVID-19 pandemic.

Strategies to exiting the COVID-19 lockdown for workplace and school: A scoping review

In an attempt to curb the COVID-19 pandemic, several countries have implemented various social restrictions, such as closing schools and asking people to work from home. Nevertheless, after months of strict quarantine, a reopening of society is required. Many countries are planning exit strategies to progressively lift the lockdown without leading to an increase in the number of COVID-19 cases. Identifying exit strategies for a safe reopening of schools and places of work is critical in informing decision-makers on the management of the COVID-19 health crisis. This scoping review describes multiple population-wide strategies, including social distancing, testing, and contact tracing. It highlights how each strategy needs to be based on both the epidemiological situation and contextualize at local circumstances to anticipate the possibility of COVID-19 resurgence. However, the retrieved evidence lacks operational solutions and are mainly based on mathematical models and derived from grey literature. There is a need to report the impact of the implementation of country-tailored strategies and assess their effectiveness through high-quality experimental studies.

On the reliability of predictions on Covid-19 dynamics: A systematic and critical review of modelling techniques

Since the emergence of the novel 2019 coronavirus pandemic in December 2019 (COVID-19), numerous modellers have used diverse techniques to assess the dynamics of transmission of the disease, predict its future course and determine the impact of different control measures. In this study, we conducted a global systematic literature review to summarize trends in the modelling techniques used for Covid-19 from January 1st, 2020 to November 30th, 2020. We further examined the accuracy and precision of predictions by comparing predicted and observed values for cumulative cases and deaths as well as uncertainties of these predictions. From an initial 4311 peer-reviewed articles and preprints found with our defined keywords, 242 were fully analysed. Most studies were done on Asian (78.93%) and European (59.09%) countries. Most of them used compartmental models (namely SIR and SEIR) (46.1%) and statistical models (growth models and time series) (31.8%) while few used artificial intelligence (6.7%), Bayesian approach (4.7%), Network models (2.3%) and Agent-based models (1.3%). For the number of cumulative cases, the ratio of the predicted over the observed values and the ratio of the amplitude of confidence interval (CI) or credibility interval (CrI) of predictions and the central value were on average larger than 1 indicating cases of inaccurate and imprecise predictions, and large variation across predictions. There was no clear difference among models used for these two ratios. In 75% of predictions that provided CI or CrI, observed values fall within the 95% CI or CrI of the cumulative cases predicted. Only 3.7% of the studies predicted the cumulative number of deaths. For 70% of the predictions, the ratio of predicted over observed cumulative deaths was less or close to 1. Also, the Bayesian model made predictions closer to reality than classical statistical models, although these differences are only suggestive due to the small number of predictions within our dataset (9 in total). In addition, we found a significant negative correlation (rho = - 0.56, p = 0.021) between this ratio and the length (in days) of the period covered by the modelling, suggesting that the longer the period covered by the model the likely more accurate the estimates tend to be. Our findings suggest that while predictions made by the different models are useful to understand the pandemic course and guide policy-making, some were relatively accurate and precise while other not.

Face Mask Use in the Community for Reducing the Spread of COVID-19: A Systematic Review

Background: Evidence is needed on the effectiveness of wearing face masks in the community to prevent SARS-CoV-2 transmission. Methods: Systematic review and meta-analysis to investigate the efficacy and effectiveness of face mask use in a community setting and to predict the effectiveness of wearing a mask. We searched MEDLINE, EMBASE, SCISEARCH, The Cochrane Library, and pre-prints from inception to 22 April 2020 without restriction by language. We rated the certainty of evidence according to Cochrane and GRADE approach. Findings: Our search identified 35 studies, including three randomized controlled trials (RCTs) (4,017 patients), 10 comparative studies (18,984 patients), 13 predictive models, nine laboratory experimental studies. For reducing infection rates, the estimates of cluster-RCTs were in favor of wearing face masks vs. no mask, but not at statistically significant levels (adjusted OR 0.90, 95% CI 0.78-1.05). Similar findings were reported in observational studies. Mathematical models indicated an important decrease in mortality when the population mask coverage is near-universal, regardless of mask efficacy. In the best-case scenario, when the mask efficacy is at 95%, the R0 can fall to 0.99 from an initial value of 16.90. Levels of mask filtration efficiency were heterogeneous, depending on the materials used (surgical mask: 45-97%). One laboratory study suggested a viral load reduction of 0.25 (95% CI 0.09-0.67) in favor of mask vs. no mask. Interpretation: The findings of this systematic review and meta-analysis support the use of face masks in a community setting. Robust randomized trials on face mask effectiveness are needed to inform evidence-based policies. PROSPERO registration: CRD42020184963.

GRADE Guidelines 30: the GRADE approach to assessing the certainty of modeled evidence-An overview in the context of health decision-making

OBJECTIVES

The objective of the study is to present the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) conceptual approach to the assessment of certainty of evidence from modeling studies (i.e., certainty associated with model outputs).

STUDY DESIGN AND SETTING

Expert consultations and an international multidisciplinary workshop informed development of a conceptual approach to assessing the certainty of evidence from models within the context of systematic reviews, health technology assessments, and health care decisions. The discussions also clarified selected concepts and terminology used in the GRADE approach and by the modeling community. Feedback from experts in a broad range of modeling and health care disciplines addressed the content validity of the approach.

RESULTS

Workshop participants agreed that the domains determining the certainty of evidence previously identified in the GRADE approach (risk of bias, indirectness, inconsistency, imprecision, reporting bias, magnitude of an effect, dose-response relation, and the direction of residual confounding) also apply when assessing the certainty of evidence from models. The assessment depends on the nature of model inputs and the model itself and on whether one is evaluating evidence from a single model or multiple models. We propose a framework for selecting the best available evidence from models: 1) developing de novo, a model specific to the situation of interest, 2) identifying an existing model, the outputs of which provide the highest certainty evidence for the situation of interest, either "off-the-shelf" or after adaptation, and 3) using outputs from multiple models. We also present a summary of preferred terminology to facilitate communication among modeling and health care disciplines.

CONCLUSION

This conceptual GRADE approach provides a framework for using evidence from models in health decision-making and the assessment of certainty of evidence from a model or models. The GRADE Working Group and the modeling community are currently developing the detailed methods and related guidance for assessing specific domains determining the certainty of evidence from models across health care-related disciplines (e.g., therapeutic decision-making, toxicology, environmental health, and health economics).

The roadmap towards elimination of lymphatic filariasis by 2030: insights from quantitative and mathematical modelling

The Global Programme to Eliminate Lymphatic Filariasis was launched in 2000 to eliminate lymphatic filariasis (LF) as a public health problem by 1) interrupting transmission through mass drug administration (MDA) and 2) offering basic care to those suffering from lymphoedema or hydrocele due to the infection. Although impressive progress has been made, the initial target year of 2020 will not be met everywhere. The World Health Organization recently proposed 2030 as the new target year for elimination of lymphatic filariasis (LF) as a public health problem. In this letter, LF modelers of the Neglected Tropical Diseases (NTDs) Modelling Consortium reflect on the proposed targets for 2030 from a quantitative perspective. While elimination as a public health problem seems technically and operationally feasible, it is uncertain whether this will eventually also lead to complete elimination of transmission. The risk of resurgence needs to be mitigated by strong surveillance after stopping interventions and sometimes perhaps additional interventions.

The use of mathematical modeling studies for evidence synthesis and guideline development: A glossary

Mathematical modeling studies are increasingly recognised as an important tool for evidence synthesis and to inform clinical and public health decision‐making, particularly when data from systematic reviews of primary studies do not adequately answer a research question. However, systematic reviewers and guideline developers may struggle with using the results of modeling studies, because, at least in part, of the lack of a common understanding of concepts and terminology between evidence synthesis experts and mathematical modellers. The use of a common terminology for modeling studies across different clinical and epidemiological research fields that span infectious and non‐communicable diseases will help systematic reviewers and guideline developers with the understanding, characterisation, comparison, and use of mathematical modeling studies. This glossary explains key terms used in mathematical modeling studies that are particularly salient to evidence synthesis and knowledge translation in clinical medicine and public health.