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.

Modeling the potential impact of administering vaccines against Clostridioides difficile infection to individuals in healthcare facilities.

BACKGROUND: A vaccine against Clostridioides difficile infection (CDI) is in development. While the vaccine has potential to both directly protect those vaccinated and mitigate transmission by reducing environmental contamination, the impact of the vaccine on C. difficile colonization remains unclear. Consequently, the transmission-reduction effect of the vaccine depends on the contribution of symptomatic CDI to overall transmission of C. difficile. METHODS: We designed a simulation model of CDI among patients in a network of 10 hospitals and nursing homes and calibrated the model using estimates of transmissibility from whole genome sequencing studies that estimated the fraction of CDI attributable to transmission from other CDI patients. We assumed the vaccine reduced the rate of progression to CDI among carriers by 25-95% after completion of a 3-dose vaccine course administered to randomly chosen patients at facility discharge. We simulated the administration of this vaccination campaign and tallied effects over 5 years. RESULTS: We estimated 30 times higher infectivity of CDI patients compared to other carriers. Simulations of the vaccination campaign produced an average reduction of 3-16 CDI cases per 1000 vaccinated patients, with 2-11 of those cases prevented among those vaccinated and 1-5 prevented among unvaccinated patients. CONCLUSIONS: Our findings demonstrate potential for a vaccine against CDI to reduce transmissions in healthcare facilities, even with no direct effect on carriage susceptibility. The vaccine's population impact will increase if received by individuals at risk for CDI onset in high-transmission settings.

Modeling Infectious Diseases in Healthcare Network (MInD-Healthcare) Framework for Describing and Reporting Multidrug-resistant Organism and Healthcare-Associated Infections Agent-based Modeling Methods.

Mathematical modeling of healthcare-associated infections and multidrug-resistant organisms improves our understanding of pathogen transmission dynamics and provides a framework for evaluating prevention strategies. One way of improving the communication among modelers is by providing a standardized way of describing and reporting models, thereby instilling confidence in the reproducibility and generalizability of such models. We updated the Overview, Design concepts, and Details protocol developed by Grimm et al [11] for describing agent-based models (ABMs) to better align with elements commonly included in healthcare-related ABMs. The Modeling Infectious Diseases in Healthcare Network (MInD-Healthcare) framework includes the following 9 key elements: (1) Purpose and scope; (2) Entities, state variables, and scales; (3) Initialization; (4) Process overview and scheduling; (5) Input data; (6) Agent interactions and organism transmission; (7) Stochasticity; (8) Submodels; and (9) Model verification, calibration, and validation. Our objective is that this framework will improve the quality of evidence generated utilizing these models.

Forecasting the 2014 West African Ebola Outbreak.

In 2014-2015, a large Ebola outbreak afflicted Liberia, Guinea, and Sierra Leone. We performed a systematic review of 26 manuscripts, published between 2014 and April 2015, that forecasted the West African Ebola outbreak while it was occurring, and we derived implications for how results could be interpreted by policymakers. Forecasted case counts varied widely. An important determinant of forecast accuracy for case counts was how far into the future predictions were made. Generally, forecasts for less than 2 months into the future tended to be more accurate than those made for more than 10 weeks into the future. The exceptions were parsimonious statistical models in which the decay of the rate of spread of the pathogen among susceptible individuals was dealt with explicitly. The most important lessons for policymakers regarding future outbreaks, when using similar modeling results, are: 1) uncertainty of forecasts will be greater in the beginning of the outbreak; 2) when data are limited, forecasts produced by models designed to inform specific decisions should be used complementarily for robust decision-making (e.g., 2 statistical models produced the most reliable case-counts forecasts for the studied Ebola outbreak but did not enable understanding of interventions' impact, whereas several compartmental models could estimate interventions' impact but required unavailable data); and 3) timely collection of essential data is necessary for optimal model use.

The Potential for Interventions in a Long-term Acute Care Hospital to Reduce Transmission of Carbapenem-Resistant Enterobacteriaceae in Affiliated Healthcare Facilities.

BACKGROUND: Carbapenem-resistant Enterobacteriaceae (CRE) are high-priority bacterial pathogens targeted for efforts to decrease transmissions and infections in healthcare facilities. Some regions have experienced CRE outbreaks that were likely amplified by frequent transmission in long-term acute care hospitals (LTACHs). Planning and funding of intervention efforts focused on LTACHs is one proposed strategy to contain outbreaks; however, the potential regional benefits of such efforts are unclear. METHODS: We designed an agent-based simulation model of patients in a regional network of 10 healthcare facilities including 1 LTACH, 3 short-stay acute care hospitals (ACHs), and 6 nursing homes (NHs). The model was calibrated to achieve realistic patient flow and CRE transmission and detection rates. We then simulated the initiation of an entirely LTACH-focused intervention in a previously CRE-free region, including active surveillance for CRE carriers and enhanced isolation of identified carriers. RESULTS: When initiating the intervention at the first clinical CRE detection in the LTACH, cumulative CRE transmissions over 5 years across all 10 facilities were reduced by 79%-93% compared to no-intervention simulations. This result was robust to changing assumptions for transmission within non-LTACH facilities and flow of patients from the LTACH. Delaying the intervention until the 20th CRE detection resulted in substantial delays in achieving optimal regional prevalence, while still reducing transmissions by 60%-79% over 5 years. CONCLUSIONS: Focusing intervention efforts on LTACHs is potentially a highly efficient strategy for reducing CRE transmissions across an entire region, particularly when implemented as early as possible in an emerging outbreak.

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.

Estimation of Severe Middle East Respiratory Syndrome Cases in the Middle East, 2012-2016.

Using data from travelers to 4 countries in the Middle East, we estimated 3,250 (95% CI 1,300-6,600) severe cases of Middle East respiratory syndrome occurred in this region during September 2012-January 2016. This number is 2.3-fold higher than the number of laboratory-confirmed cases recorded in these countries.

Exportations of Symptomatic Cases of MERS-CoV Infection to Countries outside the Middle East.

In 2012, an outbreak of infection with Middle East respiratory syndrome coronavirus (MERS-CoV), was detected in the Arabian Peninsula. Modeling can produce estimates of the expected annual number of symptomatic cases of MERS-CoV infection exported and the likelihood of exportation from source countries in the Middle East to countries outside the region.

Infectious disease modeling methods as tools for informing response to novel influenza viruses of unknown pandemic potential.

The rising importance of infectious disease modeling makes this an appropriate time for a guide for public health practitioners tasked with preparing for, and responding to, an influenza pandemic. We list several questions that public health practitioners commonly ask about pandemic influenza and match these with analytical methods, giving details on when during a pandemic the methods can be used, how long it might take to implement them, and what data are required. Although software to perform these tasks is available, care needs to be taken to understand: (1) the type of data needed, (2) the implementation of the methods, and (3) the interpretation of results in terms of model uncertainty and sensitivity. Public health leaders can use this article to evaluate the modeling literature, determine which methods can provide appropriate evidence for decision-making, and to help them request modeling work from in-house teams or academic groups.

Estimating the United States demand for influenza antivirals and the effect on severe influenza disease during a potential pandemic.

Following the detection of a novel influenza strain A(H7N9), we modeled the use of antiviral treatment in the United States to mitigate severe disease across a range of hypothetical pandemic scenarios. Our outcomes were total demand for antiviral (neuraminidase inhibitor) treatment and the number of hospitalizations and deaths averted. The model included estimates of attack rate, healthcare-seeking behavior, prescription rates, adherence, disease severity, and the potential effect of antivirals on the risks of hospitalization and death. Based on these inputs, the total antiviral regimens estimated to be available in the United States (as of April 2013) were sufficient to meet treatment needs for the scenarios considered. However, distribution logistics were not examined and should be addressed in future work. Treatment was estimated to avert many severe outcomes (5200-248,000 deaths; 4800-504,000 hospitalizations); however, large numbers remained (25,000-425,000 deaths; 580,000-3,700,000 hospitalizations), suggesting that the impact of combinations of interventions should be examined.

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.

Oseltamivir for treatment and prevention of pandemic influenza A/H1N1 virus infection in households, Milwaukee, 2009.

BACKGROUND: During an influenza pandemic, a substantial proportion of transmission is thought to occur in households. We used data on influenza progression in individuals and their contacts collected by the City of Milwaukee Health Department (MHD) to study the transmission of pandemic influenza A/H1N1 virus in 362 households in Milwaukee, WI, and the effects of oseltamivir treatment and chemoprophylaxis. METHODS: 135 households had chronological information on symptoms and oseltamivir usage for all household members. The effect of oseltamivir treatment and other factors on the household secondary attack rate was estimated using univariate and multivariate logistic regression with households as the unit of analysis. The effect of oseltamivir treatment and other factors on the individual secondary attack rate was estimated using univariate and multivariate logistic regression with individual household contacts as the unit of analysis, and a generalized estimating equations approach was used to fit the model to allow for clustering within households. RESULTS: Oseltamivir index treatment on onset day or the following day (early treatment) was associated with a 42% reduction (OR: 0.58, 95% CI: 0.19, 1.73) in the odds of one or more secondary infections in a household and a 50% reduction (OR: 0.5, 95% CI: 0.17, 1.46) in the odds of a secondary infection in individual contacts. The confidence bounds are wide due to a small sample of households with early oseltamivir index usage - in 29 such households, 5 had a secondary attack. Younger household contacts were at higher risk of infection (OR: 2.79, 95% CI: 1.50-5.20). CONCLUSIONS: Early oseltamivir treatment may be beneficial in preventing H1N1pdm influenza transmission; this may have relevance to future control measures for influenza pandemics. Larger randomized trials are needed to confirm this finding statistically.

Student behavior during a school closure caused by pandemic influenza A/H1N1.

BACKGROUND: Many schools were temporarily closed in response to outbreaks of the recently emerged pandemic influenza A/H1N1 virus. The effectiveness of closing schools to reduce transmission depends largely on student/family behavior during the closure. We sought to improve our understanding of these behaviors. METHODOLOGY/PRINCIPAL FINDINGS: To characterize this behavior, we surveyed students in grades 9-12 and parents of students in grades 5-8 about student activities during a week long closure of a school during the first months after the disease emerged. We found significant interaction with the community and other students-though less interaction with other students than during school-with the level of interaction increasing with grade. CONCLUSIONS: Our results are useful for the future design of social distancing policies and to improving the ability of modeling studies to accurately predict their impact.

Pre-dispensing of antivirals to high-risk individuals in an influenza pandemic.

We consider the net benefits of pre-dispensing antivirals to high-risk individuals during an influenza pandemic, where the measure of the benefit is the number of severe outcomes (such as deaths or hospitalizations) prevented by antivirals in the whole population. One potential benefit of pre-dispensing is that individuals to whom antivirals have been pre-dispensed may be able to initiate treatment earlier than if they had to wait to obtain and fill a prescription, reducing their risk of progression to severe disease. If this benefit exceeds the side effects of misuse for the category of individuals to whom antivirals were pre-dispensed, and if antiviral supply exceeds overall population demand (which appears relevant for several countries including US in the 2009 H1N1 pandemic), pre-dispensing a quantity of antivirals not exceeding the difference between supply and demand is always beneficial. In this study, we consider the net benefits of pre-dispensing antivirals under various scenarios, including demand exceeding supply, and derive mathematical conditions under which antiviral pre-dispensing is advantageous on balance. For individuals whose relative risk of severe outcome is high enough, such as immunosuppressed individuals (particularly children) and possibly individuals with neurological disorders, pre-dispensing is always beneficial at a given level of antiviral stockpile with modest assumptions on the relative benefit of early treatment by a pre-dispensed course, regardless of the overall population demand for antivirals during the course of an epidemic. Making additional assumptions on either the overall population demand for antivirals (which appear relevant for the 2009 H1N1 pandemic) or on the relative benefit of pre-dispensing would make pre-dispensing net beneficial with inclusion of a larger number of persons such as pregnant women and morbidly obese adults.

Use of cumulative incidence of novel influenza A/H1N1 in foreign travelers to estimate lower bounds on cumulative incidence in Mexico.

BACKGROUND: An accurate estimate of the total number of cases and severity of illness of an emerging infectious disease is required both to define the burden of the epidemic and to determine the severity of disease. When a novel pathogen first appears, affected individuals with severe symptoms are more likely to be diagnosed. Accordingly, the total number of cases will be underestimated and disease severity overestimated. This problem is manifest in the current epidemic of novel influenza A/H1N1. METHODS AND RESULTS: We used a simple approach to leverage measures of incident influenza A/H1N1 among a relatively small and well observed group of US, UK, Spanish and Canadian travelers who had visited Mexico to estimate the incidence among a much larger and less well surveyed population of Mexican residents. We estimate that a minimum of 113,000 to 375,000 cases of novel influenza A/H1N1 have occurred in Mexicans during the month of April, 2009. Such an estimate serves as a lower bound because it does not account for underreporting of cases in travelers or for nonrandom mixing between Mexican residents and visitors, which together could increase the estimates by more than an order of magnitude. CONCLUSIONS: We find that the number of cases in Mexican residents may exceed the number of confirmed cases by two to three orders of magnitude. While the extent of disease spread is greater than previously appreciated, our estimate suggests that severe disease is uncommon since the total number of cases is likely to be much larger than those of confirmed cases.

Patterns of antigenic diversity and the mechanisms that maintain them.

Many of the remaining challenges in infectious disease control involve pathogens that fail to elicit long-lasting immunity in their hosts. Antigenic variation is a common reason for this failure and a contributor to the complexity of vaccine design. Diversifying selection by the host immune system is commonly, and often correctly, invoked to explain antigenic variability in pathogens. However, there is a wide variety of patterns of antigenic variation across space and time, and within and between hosts, and we do not yet understand the determinants of these different patterns. This review describes five such patterns, taking as examples two bacteria (Streptococcus pneumoniae and Neisseria meningitidis), two viruses (influenza A and HIV-1), as well as the pathogens (taken as a group) for which antigenic variation is negligible. Pathogen-specific explanations for these patterns of diversity are critically evaluated, and the patterns are compared against predictions of theoretical models for antigenic diversity. Major remaining challenges are highlighted, including the identification of key protective antigens in bacteria, the design of vaccines to combat antigenic variability for viruses and the development of more systematic explanations for patterns of antigenic variation.