How to make epidemiological training infectious

Improving mathematical modeling of interventions to prevent healthcare-associated infections by interrupting transmission or pathogens: How common modeling assumptions about colonized individuals impact intervention effectiveness estimates

Mathematical models are used to gauge the impact of interventions for healthcare-associated infections. As with any analytic method, such models require many assumptions. Two common assumptions are that asymptomatically colonized individuals are more likely to be hospitalized and that they spend longer in the hospital per admission because of their colonization status. These assumptions have no biological basis and could impact the estimated effects of interventions in unintended ways. Therefore, we developed a model of methicillin-resistant Staphylococcus aureus transmission to explicitly evaluate the impact of these assumptions. We found that assuming that asymptomatically colonized individuals were more likely to be admitted to the hospital or spend longer in the hospital than uncolonized individuals biased results compared to a more realistic model that did not make either assumption. Results were heavily biased when estimating the impact of an intervention that directly reduced transmission in a hospital. In contrast, results were moderately biased when estimating the impact of an intervention that decolonized hospital patients. Our findings can inform choices modelers face when constructing models of healthcare-associated infection interventions and thereby improve their validity.

E-learning in clinical microbiology and infectious diseases

BACKGROUND

Becoming and staying competent is a challenge in clinical microbiology and infectious diseases because of dramatic increases in medical knowledge, discovery of new pathogens, emerging infections, new resistance mechanisms and laboratory techniques. E-learning is an effective way of meeting educational needs by providing more efficient and flexible training. E-learning resources have become more important to acquire new knowledge and skills, especially at a time of physical distancing.

OBJECTIVES

This review aims to summarize the implementation of e-learning in clinical microbiology and infectious diseases with references to existing examples and resources.

SOURCES

Literature and online resources for e-learning, online teaching/education in medical education, clinical microbiology and infectious diseases.

CONTENT

The principles and common methods of e-learning and frequently used digital tools are described. For all aspects of e-learning/distance learning, available resources and examples of applications in clinical microbiology and infectious diseases are presented.

IMPLICATIONS

The techniques, tools and resources described in this article should be considered for the development and implementation of e-learning programmes in clinical microbiology and infectious disease training.

African based researchers' output on models for the transmission dynamics of infectious diseases and public health interventions: A scoping review

BACKGROUND

Applied epidemiological models are used in predicting future trends of diseases, for the basic understanding of disease and health dynamics, and to improve the measurement of health indicators. Mapping the research outputs of epidemiological modelling studies concerned with transmission dynamics of infectious diseases and public health interventions in Africa will help to identify the areas with substantial levels of research activities, areas with gaps, and research output trends.

METHODS

A scoping review of applied epidemiological models of infectious disease studies that involved first or last authors affiliated to African institutions was conducted. Eligible studies were those concerned with the transmission dynamics of infectious diseases and public health interventions. The review was consistent with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) extension for scoping reviews. Four electronic databases were searched for peer-reviewed publications up to the end of April 2020.

RESULTS

Of the 5927 publications identified, 181 met the inclusion criteria. The review identified 143 publications with first authors having an African institutional affiliation (AIA), while 81 had both first and last authors with an AIA. The publication authors were found to be predominantly affiliated with institutions based in South Africa and Kenya. Furthermore, human immunodeficiency virus, malaria, tuberculosis, and Ebola virus disease were found to be the most researched infectious diseases. There has been a gradual increase in research productivity across Africa especially in the last ten years, with several collaborative efforts spread both within and beyond Africa.

CONCLUSIONS

Research productivity in applied epidemiological modelling studies of infectious diseases may have increased, but there remains an under-representation of African researchers as leading authors. The study findings indicate a need for the development of research capacity through supporting existing institutions in Africa and promoting research funding that will address local health priorities.

Facilitating Understanding, Modeling and Simulation of Infectious Disease Epidemics in the Age of COVID-19

Interest in the mathematical modeling of infectious diseases has increased due to the COVID-19 pandemic. However, many medical students do not have the required background in coding or mathematics to engage optimally in this approach. System dynamics is a methodology for implementing mathematical models as easy-to-understand stock-flow diagrams. Remarkably, creating stock-flow diagrams is the same process as creating the equivalent differential equations. Yet, its visual nature makes the process simple and intuitive. We demonstrate the simplicity of system dynamics by applying it to epidemic models including a model of COVID-19 mutation. We then discuss the ease with which far more complex models can be produced by implementing a model comprising eight differential equations of a Chikungunya epidemic from the literature. Finally, we discuss the learning environment in which the teaching of the epidemic modeling occurs. We advocate the widespread use of system dynamics to empower those who are engaged in infectious disease epidemiology, regardless of their mathematical background.

The Impact of Rubella Vaccine Introduction on Rubella Infection and Congenital Rubella Syndrome: A Systematic Review of Mathematical Modelling Studies

INTRODUCTION

Rubella vaccines have been used to prevent rubella and congenital rubella syndrome (CRS) in several World Health Organization (WHO) regions. Mathematical modelling studies have simulated introduction of rubella-containing vaccines (RCVs), and their results have been used to inform rubella introduction strategies in several countries. This systematic review aimed to synthesize the evidence from mathematical models regarding the impact of introducing RCVs.

METHODS

We registered the review in the international prospective register of systematic reviews (PROSPERO) with registration number CRD42020192638. Systematic review methods for classical epidemiological studies and reporting guidelines were followed as far as possible. A comprehensive search strategy was used to identify published and unpublished studies with no language restrictions. We included deterministic and stochastic models that simulated RCV introduction into the public sector vaccination schedule, with a time horizon of at least five years. Models focused only on estimating epidemiological parameters were excluded. Outcomes of interest were time to rubella and CRS elimination, trends in incidence of rubella and CRS, number of vaccinated individuals per CRS case averted, and cost-effectiveness of vaccine introduction strategies. The methodological quality of included studies was assessed using a modified risk of bias tool, and a qualitative narrative was provided, given that data synthesis was not feasible.

RESULTS

Seven studies were included from a total of 1393 records retrieved. The methodological quality was scored high for six studies and very high for one study. Quantitative data synthesis was not possible, because only one study reported point estimates and uncertainty intervals for the outcomes. All seven included studies presented trends in rubella incidence, six studies reported trends in CRS incidence, two studies reported the number vaccinated individuals per CRS case averted, and two studies reported an economic evaluation measure. Time to CRS elimination and time to rubella elimination were not reported by any of the included studies. Reported trends in CRS incidence showed elimination within five years of RCV introduction with scenarios involving mass vaccination of older children in addition to routine infant vaccination. CRS incidence was higher with RCV introduction than without RCV when public vaccine coverage was lower than 50% or only private sector vaccination was implemented. Although vaccination of children at a given age achieved slower declines in CRS incidence compared to mass campaigns targeting a wide age range, this approach resulted in the lowest number of vaccinated individuals per CRS case averted.

CONCLUSION AND RECOMMENDATIONS

We were unable to conduct data synthesis of included studies due to discrepancies in outcome reporting. However, qualitative assessment of results of individual studies suggests that vaccination of infants should be combined with vaccination of older children to achieve rapid elimination of CRS. Better outcomes are obtained when rubella vaccination is introduced into public vaccination schedules at coverage figures of 80%, as recommended by WHO, or higher. Guidelines for reporting of outcomes in mathematical modelling studies and the conduct of systematic reviews of mathematical modelling studies are required.

A multilevel analysis of the determinants of handwashing behavior among households in Eswatini: a secondary analysis of the 2014 multiple indicator cluster survey

Introduction

Handwashing with soap has received considerable attention due to its importance in the prevention and interruption of the transmission of diseases. Regardless of the positive effects of handwashing with soap, developing countries still have a low rate of handwashing.

Objective

The study aimed to determine the individual, household and community-level factors associated with handwashing behavior among households in Eswatini

Methods

Using the Eswatini Multiple Indicator Cluster Survey conducted in 2014, a secondary analysis was done of the households surveyed. A total of 1,520 households nested in communities with complete data on handwashing practices were included in the analysis. Univariate, bivariate analysis and multivariate multilevel logistic regression were used to establish the factors that were associated with handwashing behavior.

Results

The prevalence of handwashing among households was 56% in 2014. Households whose heads were aged 35–54 and 55 years and older were more likely to practice handwashing (AOR=1.88, 95% CI:1.39, 2.54); and (AOR=1.77, 95% CI: 1.205, 2.62) compared to those aged 15–34 years. Households with a pit latrine or no toilet facility at all, were less likely to practice handwashing (AOR=0.24, 95% CI: 0.17, 0.35); (AOR=0.28, 95% CI: 0.11, 0.71) respectively compared to those with a flush toilet. Region of residence was a community-level variable associated with lower odds of handwashing, with those from the Hhohho (AOR=0.22, 95% CI: 0.14, 0.35) and Manzini region (AOR=0.42, 95% CI: 0.27, 0.67) compared to Lubombo region. Households from communities where access to mass media was high were more likely to practice handwashing (AOR =1.47, 95% CI: 1.05, 2.03) compared to those from communities where access to mass media was low

Conclusion

Households headed by young adults, with pit latrine or no toilet facility at all and lived in the Hhohho and Manzini regions and with low access to mass media, should be targeted for interventions aimed at improving handwashing practices.

The Zombie Virus Pandemic: An Innovative Simulation Integrating Virology, Population Health, and Bioethics for Preclinical Medical Students

Introduction

Understanding population health in the context of infectious disease outbreaks is an important physician competency. However, identifying effective ways to engage early medical students in this content remains a challenge. We designed an innovative pandemic simulation for first-year medical students utilizing the pop culture theme of zombies.

Methods

This 2.5-hour simulation was conducted in 2018 and 2020 during students' virology course. Student teams collected and analyzed data to formulate hypotheses for the source pathogen. The teams completed reports explaining their diagnostic hypotheses, infection containment recommendations, and resource allocation recommendations. Learners completed an evaluation of the simulation through an online survey. Responses were analyzed using descriptive statistics; narrative responses were analyzed qualitatively for themes. A content analysis was performed on students' reports.

Results

Two hundred eighty-four medical students participated in this activity. Nearly all respondents agreed that the small-group format (98%, 2018 and 2020) and pace and duration (92%, 2018; 94%, 2020) were appropriate and that the activity was intellectually stimulating (97%, 2018; 96%, 2020). Learner engagement measures were high (90%-97%, 2018; 83%-96%, 2020). Analysis of students' reports revealed evidence of cognitive integration of virology, population health, and bioethics concepts, including integration of new learning content.

Discussion

Collaborative problem-solving during a simulated zombie-themed pandemic provided preclinical medical students with an engaging opportunity to integrate virology, population health, and bioethics concepts. Implementing this event required advanced planning, use of multiple spaces, learning materials preparation, and recruitment of several faculty, staff, and actors.

How to choose the best control strategy? Mathematical models as a tool for pre-intervention evaluation on a macroparasitic disease

During the last century, emerging diseases have increased in number, posing a severe threat for human health. Zoonoses, in particular, represent the 60% of emerging diseases, and are a big challenge for public health due to the complexity of their dynamics. Mathematical models, by allowing an a priori analysis of dynamic systems and the simulation of different scenarios at once, may represent an efficient tool for the determination of factors and phenomena involved in zoonotic infection cycles, but are often underexploited in public health. In this context, we developed a deterministic mathematical model to compare the efficacy of different intervention strategies aimed at reducing environmental contamination by macroparasites, using raccoons (Procyon lotor) and their zoonotic parasite Bayilsascaris procyonis as a model system. The three intervention strategies simulated are raccoon depopulation, anthelmintic treatment of raccoons and faeces removal. Our results show that all these strategies are able to eliminate the parasite egg population from the environment, but they are effective only above specific threshold coverages. Host removal and anthelmintic treatment showed the fastest results in eliminating the egg population, but anthelmintic treatment requires a higher effort to reach an effective result compared to host removal. Our simulations show that mathematical models can help to shed light on the dynamics of communicable infectious diseases, and give specific guidelines to contain B. procyonis environmental contamination in native, as well as in new, areas of parasite emergence. In particular, the present study highlights that identifying in advance the appropriate treatment coverage is fundamental to achieve the desired results, allowing for the implementation of cost- and time-effective intervention strategies.

How Modelling Can Enhance the Analysis of Imperfect Epidemic Data

Mathematical models play an increasingly important role in our understanding of the transmission and control of infectious diseases. Here, we present concrete examples illustrating how mathematical models, paired with rigorous statistical methods, are used to parse data of different levels of detail and breadth and estimate key epidemiological parameters (e.g., transmission and its determinants, severity, impact of interventions, drivers of epidemic dynamics) even when these parameters are not directly measurable, when data are limited, and when the epidemic process is only partially observed. Finally, we assess the hurdles to be taken to increase availability and applicability of these approaches in an effort to ultimately enhance their public health impact.

Many data can be used to estimate the transmission potential of a pathogen, including descriptions of the transmission chains, human cluster sizes, sources of infection, and epidemic curves.

An important agenda in public health is understanding the impact of control methods. However, the dynamic nature of epidemics makes this task challenging. Models can disentangle the natural course of outbreaks from the effect of external factors.

In the absence of reliable surveillance data, models can reconstruct epidemic history by combining age-specific seroprevalence data with an understanding of the natural history of infection.

Mechanisms of immunity are hard to observe at an individual level, yet they affect population-level dynamics. Models can tease out such signatures.

Morbidity and mortality can be difficult to estimate when many infections are unobserved and severe infections are reported more often. Models can be used to correct for under-reporting and selection bias.

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.

An infectious way to teach students about outbreaks

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An updated epidemiological teaching exercise was developed.

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Students participate in an outbreak that they subsequently analyse.

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Data from five years of consecutive student cohorts is presented.

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An R package and practical are developed that improve the pedagogical experience.

The study of infectious disease outbreaks is required to train today’s epidemiologists. A typical way to introduce and explain key epidemiological concepts is through the analysis of a historical outbreak. There are, however, few training options that explicitly utilise real-time simulated stochastic outbreaks where the participants themselves comprise the dataset they subsequently analyse. In this paper, we present a teaching exercise in which an infectious disease outbreak is simulated over a five-day period and subsequently analysed. We iteratively developed the teaching exercise to offer additional insight into analysing an outbreak. An R package for visualisation, analysis and simulation of the outbreak data was developed to accompany the practical to reinforce learning outcomes. Computer simulations of the outbreak revealed deviations from observed dynamics, highlighting how simplifying assumptions conventionally made in mathematical models often differ from reality. Here we provide a pedagogical tool for others to use and adapt in their own settings.

Reassessment of HIV-1 acute phase infectivity: accounting for heterogeneity and study design with simulated cohorts

BACKGROUND

The infectivity of the HIV-1 acute phase has been directly measured only once, from a retrospectively identified cohort of serodiscordant heterosexual couples in Rakai, Uganda. Analyses of this cohort underlie the widespread view that the acute phase is highly infectious, even more so than would be predicted from its elevated viral load, and that transmission occurring shortly after infection may therefore compromise interventions that rely on diagnosis and treatment, such as antiretroviral treatment as prevention (TasP). Here, we re-estimate the duration and relative infectivity of the acute phase, while accounting for several possible sources of bias in published estimates, including the retrospective cohort exclusion criteria and unmeasured heterogeneity in risk.

METHODS AND FINDINGS

We estimated acute phase infectivity using two approaches. First, we combined viral load trajectories and viral load-infectivity relationships to estimate infectivity trajectories over the course of infection, under the assumption that elevated acute phase infectivity is caused by elevated viral load alone. Second, we estimated the relative hazard of transmission during the acute phase versus the chronic phase (RHacute) and the acute phase duration (dacute) by fitting a couples transmission model to the Rakai retrospective cohort using approximate Bayesian computation. Our model fit the data well and accounted for characteristics overlooked by previous analyses, including individual heterogeneity in infectiousness and susceptibility and the retrospective cohort's exclusion of couples that were recorded as serodiscordant only once before being censored by loss to follow-up, couple dissolution, or study termination. Finally, we replicated two highly cited analyses of the Rakai data on simulated data to identify biases underlying the discrepancies between previous estimates and our own. From the Rakai data, we estimated RHacute = 5.3 (95% credibility interval [95% CrI]: 0.79-57) and dacute = 1.7 mo (95% CrI: 0.55-6.8). The wide credibility intervals reflect an inability to distinguish a long, mildly infectious acute phase from a short, highly infectious acute phase, given the 10-mo Rakai observation intervals. The total additional risk, measured as excess hazard-months attributable to the acute phase (EHMacute) can be estimated more precisely: EHMacute = (RHacute - 1) × dacute, and should be interpreted with respect to the 120 hazard-months generated by a constant untreated chronic phase infectivity over 10 y of infection. From the Rakai data, we estimated that EHMacute = 8.4 (95% CrI: -0.27 to 64). This estimate is considerably lower than previously published estimates, and consistent with our independent estimate from viral load trajectories, 5.6 (95% confidence interval: 3.3-9.1). We found that previous overestimates likely stemmed from failure to account for risk heterogeneity and bias resulting from the retrospective cohort study design. Our results reflect the interaction between the retrospective cohort exclusion criteria and high (47%) rates of censorship amongst incident serodiscordant couples in the Rakai study due to loss to follow-up, couple dissolution, or study termination. We estimated excess physiological infectivity during the acute phase from couples data, but not the proportion of transmission attributable to the acute phase, which would require data on the broader population's sexual network structure.

CONCLUSIONS

Previous EHMacute estimates relying on the Rakai retrospective cohort data range from 31 to 141. Our results indicate that these are substantial overestimates of HIV-1 acute phase infectivity, biased by unmodeled heterogeneity in transmission rates between couples and by inconsistent censoring. Elevated acute phase infectivity is therefore less likely to undermine TasP interventions than previously thought. Heterogeneity in infectiousness and susceptibility may still play an important role in intervention success and deserves attention in future analyses.

Estimating the per-exposure effect of infectious disease interventions

The average effect of an infectious disease intervention (eg, a vaccine) varies across populations with different degrees of exposure to the pathogen. As a result, many investigators favor a per-exposure effect measure that is considered independent of the population level of exposure and that can be used in simulations to estimate the total disease burden averted by an intervention across different populations. However, while per-exposure effects are frequently estimated, the quantity of interest is often poorly defined, and assumptions in its calculation are typically left implicit. In this article, we build upon work by Halloran and Struchiner (Epidemiology. 1995;6:142-151) to develop a formal definition of the per-exposure effect and discuss conditions necessary for its unbiased estimation. With greater care paid to the parameterization of transmission models, their results can be better understood and can thereby be of greater value to decision-makers.

Coexisting with wildlife in transfrontier conservation areas in Zimbabwe: cattle owners' awareness of disease risks and perceptions of the role played by wildlife

Diseases transmitted between wildlife and livestock may have significant impacts on local farmers' health, livestock health and productivity, overall national economies, and conservation initiatives, such as Transfrontier Conservation Areas in Southern Africa. However, little is known on local farmers' awareness of the potential risks, and how they perceive the role played by wildlife in the epidemiology of these diseases. We investigated the knowledge base regarding livestock diseases of local cattle owners living at the periphery of conservation areas within the Great Limpopo TFCA and the Kavango-Zambezi TFCA in Zimbabwe, using free-listing and semi-structured questionnaires during dipping sessions. The results suggest that information related to cattle diseases circulates widely between cattle farmers, including between different socio-cultural groups, using English and vernacular languages. Most respondents had an accurate perception of the epidemiology of diseases affecting their livestock, and their perception of the potential role played by wildlife species was usually in agreement with current state of veterinary knowledge. However, we found significant variations in the cultural importance of livestock diseases between sites, and owners' perceptions were not directly related with the local abundance of wildlife. As the establishment of TFCAs will potentially increase the risk of Transboundary Animal Diseases, we recommend an increased participation of communities at a local level in the prioritisation of livestock diseases control and surveillance, including zoonoses.