Maternal pertussis immunization and the blunting of routine vaccine effectiveness: a meta-analysis and modeling study.

A key goal of pertussis control is to protect infants too young to be vaccinated, the age group most vulnerable to this highly contagious respiratory infection. In the last decade, maternal immunization has been deployed in many countries, successfully reducing pertussis in this age group. Because of immunological blunting, however, this strategy may erode the effectiveness of primary vaccination at later ages. Here, we systematically reviewed the literature on the relative risk (RR) of pertussis after primary immunization of infants born to vaccinated vs. unvaccinated mothers. The four studies identified had ≤6 years of follow-up and large statistical uncertainty (meta-analysis weighted mean RR: 0.71, 95% CI: 0.38-1.32). To interpret this evidence, we designed a new mathematical model with explicit blunting mechanisms and evaluated maternal immunization's short- and long-term impact on pertussis transmission dynamics. We show that transient dynamics can mask blunting for at least a decade after rolling out maternal immunization. Hence, the current epidemiological evidence may be insufficient to rule out modest reductions in the effectiveness of primary vaccination. Irrespective of this potential collateral cost, we predict that maternal immunization will remain effective at protecting unvaccinated newborns, supporting current public health recommendations.

Delineating the Seasonality of Varicella and Its Association With Climate in the Tropical Country of Colombia.

BACKGROUND: Varicella causes a major health burden in many low- to middle-income countries located in tropical regions. Because of the lack of surveillance data, however, the epidemiology of varicella in these regions remains uncharacterized. In this study, based on an extensive dataset of weekly varicella incidence in children ≤10 during 2011-2014 in 25 municipalities, we aimed to delineate the seasonality of varicella across the diverse tropical climates of Colombia. METHODS: We used generalized additive models to estimate varicella seasonality, and we used clustering and matrix correlation methods to assess its correlation with climate. Furthermore, we developed a mathematical model to examine whether including the effect of climate on varicella transmission could reproduce the observed spatiotemporal patterns. RESULTS: Varicella seasonality was markedly bimodal, with latitudinal changes in the peaks' timing and amplitude. This spatial gradient strongly correlated with specific humidity (Mantel statistic = 0.412, P = .001) but not temperature (Mantel statistic = 0.077, P = .225). The mathematical model reproduced the observed patterns not only in Colombia but also México, and it predicted a latitudinal gradient in Central America. CONCLUSIONS: These results demonstrate large variability in varicella seasonality across Colombia and suggest that spatiotemporal humidity fluctuations can explain the calendar of varicella epidemics in Colombia, México, and potentially in Central America.

Using LASSO Regression to Estimate the Population-Level Impact of Pneumococcal Conjugate Vaccines.

Pneumococcal conjugate vaccines (PCVs) protect against diseases caused by Streptococcus pneumoniae, such as meningitis, bacteremia, and pneumonia. It is challenging to estimate their population-level impact due to the lack of a perfect control population and the subtleness of signals when the endpoint-such as all-cause pneumonia-is nonspecific. Here we present a new approach for estimating the impact of PCVs: using least absolute shrinkage and selection operator (LASSO) regression to select variables in a synthetic control model to predict the counterfactual outcome for vaccine impact inference. We first used a simulation study based on hospitalization data from Mexico (2000-2013) to test the performance of LASSO and established methods, including the synthetic control model with Bayesian variable selection (SC). We found that LASSO achieved accurate and precise estimation, even in complex simulation scenarios where the association between the outcome and all control variables was noncausal. We then applied LASSO to real-world hospitalization data from Chile (2001-2012), Ecuador (2001-2012), Mexico (2000-2013), and the United States (1996-2005), and found that it yielded estimates of vaccine impact similar to SC. The LASSO method is accurate and easily implementable and can be applied to study the impact of PCVs and other vaccines.

The interactions of SARS-CoV-2 with cocirculating pathogens: Epidemiological implications and current knowledge gaps.

Despite the availability of effective vaccines, the persistence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) suggests that cocirculation with other pathogens and resulting multiepidemics (of, for example, COVID-19 and influenza) may become increasingly frequent. To better forecast and control the risk of such multiepidemics, it is essential to elucidate the potential interactions of SARS-CoV-2 with other pathogens; these interactions, however, remain poorly defined. Here, we aimed to review the current body of evidence about SARS-CoV-2 interactions. Our review is structured in four parts. To study pathogen interactions in a systematic and comprehensive way, we first developed a general framework to capture their major components: sign (either negative for antagonistic interactions or positive for synergistic interactions), strength (i.e., magnitude of the interaction), symmetry (describing whether the interaction depends on the order of infection of interacting pathogens), duration (describing whether the interaction is short-lived or long-lived), and mechanism (e.g., whether interaction modifies susceptibility to infection, transmissibility of infection, or severity of disease). Second, we reviewed the experimental evidence from animal models about SARS-CoV-2 interactions. Of the 14 studies identified, 11 focused on the outcomes of coinfection with nonattenuated influenza A viruses (IAVs), and 3 with other pathogens. The 11 studies on IAV used different designs and animal models (ferrets, hamsters, and mice) but generally demonstrated that coinfection increased disease severity compared with either monoinfection. By contrast, the effect of coinfection on the viral load of either virus was variable and inconsistent across studies. Third, we reviewed the epidemiological evidence about SARS-CoV-2 interactions in human populations. Although numerous studies were identified, only a few were specifically designed to infer interaction, and many were prone to multiple biases, including confounding. Nevertheless, their results suggested that influenza and pneumococcal conjugate vaccinations were associated with a reduced risk of SARS-CoV-2 infection. Finally, fourth, we formulated simple transmission models of SARS-CoV-2 cocirculation with an epidemic viral pathogen or an endemic bacterial pathogen, showing how they can naturally incorporate the proposed framework. More generally, we argue that such models, when designed with an integrative and multidisciplinary perspective, will be invaluable tools to resolve the substantial uncertainties that remain about SARS-CoV-2 interactions.

Immunological heterogeneity informs estimation of the durability of vaccine protection.

Deciphering the properties of vaccines against an emerging pathogen is essential for optimizing immunization strategies. Early after vaccine roll-out, however, uncertainties about vaccine immunity raise the question of how much time is needed to estimate these properties, particularly the durability of vaccine protection. Here we designed a simulation study, based on a generic transmission model of vaccination, to simulate the impact of a breadth of vaccines with different mean (range: 10 months-2 years) and variability (coefficient of variation range: 50-100%) of the duration of protection. Focusing on the dynamics of SARS-CoV-2 in the year after start of mass immunization in Germany as a case study, we then assessed how confidently the duration of protection could be estimated under a range of epidemiological scenarios. We found that lower mean and higher heterogeneity facilitated estimation of the duration of vaccine protection. Across the vaccines tested, rapid waning and high heterogeneity permitted complete identification of the duration of protection; by contrast, slow waning and low heterogeneity allowed only estimation of the fraction of vaccinees with rapid loss of immunity. These findings suggest that limited epidemiological data can inform the duration of vaccine immunity. More generally, they highlight the need to carefully consider immunological heterogeneity when designing transmission models to evaluate vaccines.

The pitfalls of inferring virus-virus interactions from co-detection prevalence data: application to influenza and SARS-CoV-2.

There is growing experimental evidence that many respiratory viruses-including influenza and SARS-CoV-2-can interact, such that their epidemiological dynamics may not be independent. To assess these interactions, standard statistical tests of independence suggest that the prevalence ratio-defined as the ratio of co-infection prevalence to the product of single-infection prevalences-should equal unity for non-interacting pathogens. As a result, earlier epidemiological studies aimed to estimate the prevalence ratio from co-detection prevalence data, under the assumption that deviations from unity implied interaction. To examine the validity of this assumption, we designed a simulation study that built on a broadly applicable epidemiological model of co-circulation of two emerging or seasonal respiratory viruses. By focusing on the pair influenza-SARS-CoV-2, we first demonstrate that the prevalence ratio systematically underestimates the strength of interaction, and can even misclassify antagonistic or synergistic interactions that persist after clearance of infection. In a global sensitivity analysis, we further identify properties of viral infection-such as a high reproduction number or a short infectious period-that blur the interaction inferred from the prevalence ratio. Altogether, our results suggest that ecological or epidemiological studies based on co-detection prevalence data provide a poor guide to assess interactions among respiratory viruses.

The Impact of Cocirculating Pathogens on Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2)/Coronavirus Disease 2019 Surveillance: How Concurrent Epidemics May Introduce Bias and Decrease the Observed SARS-CoV-2 Percentage Positivity.

BACKGROUND: Circulation of seasonal non-severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) respiratory viruses with syndromic overlap during the coronavirus disease 2019 (COVID-19) pandemic may alter the quality of COVID-19 surveillance, with possible consequences for real-time analysis and delay in implementation of control measures. METHODS: Using a multipathogen susceptible-exposed-infectious-recovered (SEIR) transmission model formalizing cocirculation of SARS-CoV-2 and another respiratory virus, we assessed how an outbreak of secondary virus may affect 2 COVID-19 surveillance indicators: testing demand and positivity. Using simulation, we assessed to what extent the use of multiplex polymerase chain reaction tests on a subsample of symptomatic individuals can help correct the observed SARS-CoV-2 percentage positivity and improve surveillance quality. RESULTS: We find that a non-SARS-CoV-2 epidemic strongly increases SARS-CoV-2 daily testing demand and artificially reduces the observed SARS-CoV-2 percentage positivity for the duration of the outbreak. We estimate that performing 1 multiplex test for every 1000 COVID-19 tests on symptomatic individuals could be sufficient to maintain surveillance of other respiratory viruses in the population and correct the observed SARS-CoV-2 percentage positivity. CONCLUSIONS: This study showed that cocirculating respiratory viruses can distort SARS-CoV-2 surveillance. Correction of the positivity rate can be achieved by using multiplex polymerase chain reaction tests, and a low number of samples is sufficient to avoid bias in SARS-CoV-2 surveillance.

Estimating the impact of influenza on the epidemiological dynamics of SARS-CoV-2.

As in past pandemics, co-circulating pathogens may play a role in the epidemiology of coronavirus disease 2019 (COVID-19), caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In particular, experimental evidence indicates that influenza infection can up-regulate the expression of ACE2-the receptor of SARS-CoV-2 in human cells-and facilitate SARS-CoV-2 infection. Here we hypothesized that influenza impacted the epidemiology of SARS-CoV-2 during the early 2020 epidemic of COVID-19 in Europe. To test this hypothesis, we developed a population-based model of SARS-CoV-2 transmission and of COVID-19 mortality, which simultaneously incorporated the impact of non-pharmaceutical control measures and of influenza on the epidemiological dynamics of SARS-CoV-2. Using statistical inference methods based on iterated filtering, we confronted this model with mortality incidence data in four European countries (Belgium, Italy, Norway, and Spain) to systematically test a range of assumptions about the impact of influenza. We found consistent evidence for a 1.8-3.4-fold (uncertainty range across countries: 1.1 to 5.0) average population-level increase in SARS-CoV-2 transmission associated with influenza during the period of co-circulation. These estimates remained robust to a variety of alternative assumptions regarding the epidemiological traits of SARS-CoV-2 and the modeled impact of control measures. Although further confirmatory evidence is required, our results suggest that influenza could facilitate the spread and hamper effective control of SARS-CoV-2. More generally, they highlight the possible role of co-circulating pathogens in the epidemiology of COVID-19.

Transmissibility and pathogenicity of the emerging meningococcal serogroup W sequence type-11 complex South American strain: a mathematical modeling study.

BACKGROUND: The recent emergence of strains belonging to the meningococcal serogroup W (MenW) sequence type-11 clonal complex and descending from the South American sub-lineage (MenW:cc11/SA) has caused significant shifts in the epidemiology of meningococcal disease worldwide. Although MenW:cc11/SA is deemed highly transmissible and invasive, its epidemiological characteristics have not yet been quantified. METHODS: We designed a mathematical model of MenW transmission, carriage, and infection to analyze the recent epidemiology of invasive disease caused by MenW:cc11/SA strains and by other MenW strains in England and in France. We confronted that model with age-stratified incidence data to estimate the transmissibility and the invasiveness of MenW:cc11/SA in England, using the data in France as a validation cohort. RESULTS: During the epidemiological years 2010/2011-2014/2015 in England, the transmissibility of MenW:cc11/SA relative to that of other MenW strains was estimated at 1.20 (95% confidence interval, 1.15 to 1.26). The relative invasiveness of MenW:cc11/SA was also found to exceed unity and to increase with age, with estimates ranging from 4.0 (1.6 to 9.7) in children aged 0-4 years to 20 (6 to 34) in adults aged ≥ 25 years. In France, the model calibrated in England correctly reproduced the early increase of MenW:cc11/SA disease during 2012/2013-2016/2017. Most recent surveillance data, however, indicated a decline in MenW:cc11/SA disease. In both countries, our results suggested that the transmission of MenW:cc11/SA carriage possibly started several months before the first reported case of MenW:cc11/SA disease. DISCUSSION: Our results confirm earlier suggestions about the transmission and the pathogenic potential of MenW:cc11/SA. The main limitation of our study was the lack of age-specific MenW carriage data to confront our model predictions with. Furthermore, the lesser model fit to the most recent data in France suggests that the predictive accuracy of our model might be limited to 5-6 years. CONCLUSIONS: Our study provides the first estimates of the transmissibility and of the invasiveness of MenW:cc11/SA. Such estimates may be useful to anticipate changes in the epidemiology of MenW and to adapt vaccination strategies. Our results also point to silent, prolonged transmission of MenW:cc11/SA carriage, with potentially important implications for epidemic preparedness.

Duration of Immunity and Effectiveness of Diphtheria-Tetanus-Acellular Pertussis Vaccines in Children.

Importance: The United States has experienced a nationwide resurgence of pertussis since the mid-1970s, despite high estimated vaccine coverage. Short-lived immunity induced by diphtheria-tetanus-acellular pertussis (DTaP) vaccines in young children is widely believed to be responsible for this growing burden, but the duration of protection conferred by DTaP vaccines remains incompletely quantified. Objective: To assess the duration of immunity and the effectiveness of DTaP vaccines in US children. Design, Setting, and Participants: A mathematical, age-structured model of pertussis transmission, previously validated empirically on incidence data in Massachusetts, was used in this simulation study to assess the duration of DTaP immunity most consistent with the empirical values of the relative increase in the odds of acquiring pertussis from recent epidemiologic studies in the United States. The study included 5 simulated cohorts of children born between January 1, 2001, and December 31, 2005, followed up between the ages of 5 and 9 years (study period, January 1, 2006, to December 31, 2014). Statistical analysis was performed from May 1 to December 1, 2017. Interventions: Vaccination with DTaP according to the US immunization schedule, with a range of assumptions regarding the degree of waning immunity. Main Outcomes and Measures: Vaccine effectiveness and relative change in the odds of acquiring pertussis (odds ratio) in children aged 5 to 9 years, duration of DTaP immunity, and vaccine population-level impact. Results: This study found a marked association between the degree of waning immunity, vaccine effectiveness, and the odds ratio. Counterintuitively, the odds ratio was positively associated with vaccine effectiveness, as a consequence of nonlinear, age-assortative transmission dynamics. Based on the empirical odds ratios (1.33; 95% CI, 1.23-1.43), it was estimated that vaccine effectiveness exceeded 75% in children aged 5 to 9 years and that more than 65% of children remained immune to pertussis 5 years after the last DTaP dose. Conclusions and Relevance: The results of this study suggest that temporal trends in the odds of acquiring pertussis are an unreliable measure of the durability of vaccine-induced protection. They further demonstrate that DTaP vaccines confer imperfect, but long-lived protection. Control strategies should be based on the best available estimates of vaccine properties and the age structure of the transmission network.

Unraveling the seasonal epidemiology of pneumococcus.

Infections caused by Streptococcus pneumoniae-including invasive pneumococcal diseases (IPDs)-remain a significant public health concern worldwide. The marked winter seasonality of IPDs is a striking, but still enigmatic aspect of pneumococcal epidemiology in nontropical climates. Here we confronted age-structured dynamic models of carriage transmission and disease with detailed IPD incidence data to test a range of hypotheses about the components and the mechanisms of pneumococcal seasonality. We find that seasonal variations in climate, influenza-like illnesses, and interindividual contacts jointly explain IPD seasonality. We show that both the carriage acquisition rate and the invasion rate vary seasonally, acting in concert to generate the marked seasonality typical of IPDs. We also find evidence that influenza-like illnesses increase the invasion rate in an age-specific manner, with a more pronounced effect in the elderly than in other demographics. Finally, we quantify the potential impact of seasonally timed interventions, a type of control measures that exploit pneumococcal seasonality to help reduce IPDs. Our findings shed light on the epidemiology of pneumococcus and may have notable implications for the control of pneumococcal infections.

Response to Comment on "The impact of past vaccination coverage and immunity on pertussis resurgence".

We present new evidence that the immunity conferred against pertussis by the DTaP acellular vaccine wanes more slowly than widely believed.

The impact of past vaccination coverage and immunity on pertussis resurgence.

The resurgence of pertussis over the past decades has resulted in incidence levels not witnessed in the United States since the 1950s. The underlying causes have been the subject of much speculation, with particular attention paid to the shortcomings of the latest generation of vaccines. We formulated transmission models comprising competing hypotheses regarding vaccine failure and challenged them to explain 16 years of highly resolved incidence data from Massachusetts, United States. Our results suggest that the resurgence of pertussis is a predictable consequence of incomplete historical coverage with an imperfect vaccine that confers slowly waning immunity. We found evidence that the vaccine itself is effective at reducing overall transmission, yet that routine vaccination alone would be insufficient for elimination of the disease. Our results indicated that the core transmission group is schoolchildren. Therefore, efforts aimed at curtailing transmission in the population at large, and especially in vulnerable infants, are more likely to succeed if targeted at schoolchildren, rather than adults.

Characterizing and Comparing the Seasonality of Influenza-Like Illnesses and Invasive Pneumococcal Diseases Using Seasonal Waveforms.

The seasonalities of influenza-like illnesses (ILIs) and invasive pneumococcal diseases (IPDs) remain incompletely understood. Experimental evidence indicates that influenza-virus infection predisposes to pneumococcal disease, so that a correspondence in the seasonal patterns of ILIs and IPDs might exist at the population level. We developed a method to characterize seasonality by means of easily interpretable summary statistics of seasonal shape-or seasonal waveforms. Nonlinear mixed-effects models were used to estimate those waveforms based on weekly case reports of ILIs and IPDs in 5 regions spanning continental France from July 2000 to June 2014. We found high variability of ILI seasonality, with marked fluctuations of peak amplitudes and peak times, but a more conserved epidemic duration. In contrast, IPD seasonality was best modeled by a markedly regular seasonal baseline, punctuated by 2 winter peaks in late December to early January and January to February. Comparing ILI and IPD seasonal waveforms, we found indication of a small, positive correlation. Direct models regressing IPDs on ILIs provided comparable results, even though they estimated moderately larger associations. The method proposed is broadly applicable to diseases with unambiguous seasonality and is well-suited to analyze spatially or temporally grouped data, which are common in epidemiology.

An agent-based model simulation of influenza interactions at the host level: insight into the influenza-related burden of pneumococcal infections.

BACKGROUND: Host-level influenza virus-respiratory pathogen interactions are often reported. Although the exact biological mechanisms involved remain unelucidated, secondary bacterial infections are known to account for a large part of the influenza-associated burden, during seasonal and pandemic outbreaks. Those interactions probably impact the microorganisms' transmission dynamics and the influenza public health toll. Mathematical models have been widely used to examine influenza epidemics and the public health impact of control measures. However, most influenza models overlooked interaction phenomena and ignored other co-circulating pathogens. METHODS: Herein, we describe a novel agent-based model (ABM) of influenza transmission during interaction with another respiratory pathogen. The interacting microorganism can persist in the population year round (endemic type, e.g. respiratory bacteria) or cause short-term annual outbreaks (epidemic type, e.g. winter respiratory viruses). The agent-based framework enables precise formalization of the pathogens' natural histories and complex within-host phenomena. As a case study, this ABM is applied to the well-known influenza virus-pneumococcus interaction, for which several biological mechanisms have been proposed. Different mechanistic hypotheses of interaction are simulated and the resulting virus-induced pneumococcal infection (PI) burden is assessed. RESULTS: This ABM generates realistic data for both pathogens in terms of weekly incidences of PI cases, carriage rates, epidemic size and epidemic timing. Notably, distinct interaction hypotheses resulted in different transmission patterns and led to wide variations of the associated PI burden. Interaction strength was also of paramount importance: when influenza increased pneumococcus acquisition, 4-27% of the PI burden during the influenza season was attributable to influenza depending on the interaction strength. CONCLUSIONS: This open-source ABM provides new opportunities to investigate influenza interactions from a theoretical point of view and could easily be extended to other pathogens. It provides a unique framework to generate in silico data for different scenarios and thereby test mechanistic hypotheses.

Impact of pneumococcal conjugate vaccines on pneumococcal meningitis cases in France between 2001 and 2014: a time series analysis.

BACKGROUND: Pneumococcal meningitis (PM) is a major invasive pneumococcal disease. Two pneumococcal conjugate vaccines (PCVs) have been introduced in France: PCV7 was recommended in 2003 and replaced in 2010 by PCV13, which has six additional serotypes. The impact of introducing those vaccines on the evolution of PM case numbers and serotype distributions in France from 2001 to 2014 is assessed herein. METHODS: Data on 5166 Streptococcus pneumoniae strains isolated from cerebrospinal fluid between 2001 and 2014 in the 22 regions of France were obtained from the National Reference Center for Pneumococci. The effects of the different vaccination campaigns were estimated using time series analyses through autoregressive moving-average models with exogenous variables ("flu-like" syndromes incidence) and intervention functions. Intervention functions used 11 dummy variables representing each post vaccine epidemiological period. The evolution of serotype distributions was assessed for the entire population and the two most exposed age groups (<5 and > 64 years old). RESULTS: For the first time since PCV7 introduction in 2003, total PM cases decreased significantly after starting PCV13 use: -7.1 (95% CI, -10.85 to -3.35) cases per month during 2013-2014, and was confirmed in children < 5 years old (-3.5; 95% CI, -4.81 to -2.13) and adults > 64 years old (-2.0; 95% CI, -3.36 to -0.57). During 2012-2014, different non-vaccine serotypes emerged: 12F, 24F in the entire population and children, 6C in the elderly; serotypes 3 and 19F persisted in the entire population. CONCLUSIONS: Unlike other European countries, the total PM cases in France declined only after introduction of PCV13. This suggests that vaccine pressure alone does not explain pneumococcal epidemiological changes and that other factors could play a role. Serotype distribution had changed substantially compared to the pre-vaccine era, as in other European countries, but very differently from the US. A highly reactive surveillance system is thus necessary not only to monitor evolutions due to vaccine pressure and to verify the local serotypic appropriateness of new higher-valent pneumococcal vaccines, but also to recognise and prevent unexpected changes due to other internal or external factors.

The pertussis enigma: reconciling epidemiology, immunology and evolution.

Pertussis, a highly contagious respiratory infection, remains a public health priority despite the availability of vaccines for 70 years. Still a leading cause of mortality in developing countries, pertussis has re-emerged in several developed countries with high vaccination coverage. Resurgence of pertussis in these countries has routinely been attributed to increased awareness of the disease, imperfect vaccinal protection or high infection rates in adults. In this review, we first present 1980-2012 incidence data from 63 countries and show that pertussis resurgence is not universal. We further argue that the large geographical variation in trends probably precludes a simple explanation, such as the transition from whole-cell to acellular pertussis vaccines. Reviewing available evidence, we then propose that prevailing views on pertussis epidemiology are inconsistent with both historical and contemporary data. Indeed, we summarize epidemiological evidence showing that natural infection and vaccination both appear to provide long-term protection against transmission and disease, so that previously infected or vaccinated adults contribute little to overall transmission at a population level. Finally, we identify several promising avenues that may lead to a consistent explanation of global pertussis epidemiology and to more effective control strategies.

Avoidable errors in the modelling of outbreaks of emerging pathogens, with special reference to Ebola.

As an emergent infectious disease outbreak unfolds, public health response is reliant on information on key epidemiological quantities, such as transmission potential and serial interval. Increasingly, transmission models fit to incidence data are used to estimate these parameters and guide policy. Some widely used modelling practices lead to potentially large errors in parameter estimates and, consequently, errors in model-based forecasts. Even more worryingly, in such situations, confidence in parameter estimates and forecasts can itself be far overestimated, leading to the potential for large errors that mask their own presence. Fortunately, straightforward and computationally inexpensive alternatives exist that avoid these problems. Here, we first use a simulation study to demonstrate potential pitfalls of the standard practice of fitting deterministic models to cumulative incidence data. Next, we demonstrate an alternative based on stochastic models fit to raw data from an early phase of 2014 West Africa Ebola virus disease outbreak. We show not only that bias is thereby reduced, but that uncertainty in estimates and forecasts is better quantified and that, critically, lack of model fit is more readily diagnosed. We conclude with a short list of principles to guide the modelling response to future infectious disease outbreaks.

Limits of patient isolation measures to control extended-spectrum beta-lactamase-producing Enterobacteriaceae: model-based analysis of clinical data in a pediatric ward.

BACKGROUND: Extended-spectrum beta-lactamase-producing Enterobacteriaceae (ESBL-E) are a growing concern in hospitals and the community. How to control the nosocomial ESBL-E transmission is a matter of debate. Contact isolation of patients has been recommended but evidence supporting it in non-outbreak settings has been inconclusive. METHODS: We used stochastic transmission models to analyze retrospective observational data from a two-phase intervention in a pediatric ward, successively implementing single-room isolation and patient cohorting in an isolation ward, combined with active ESBL-E screening. RESULTS: For both periods, model estimates suggested reduced transmission from isolated/cohorted patients. However, most of the incidence originated from sporadic sources (i.e. independent of cross-transmission), unaffected by the isolation measures. When sporadic sources are high, our model predicted that even substantial efforts to prevent transmission from carriers would have limited impact on ESBL-E rates. CONCLUSIONS: Our results provide evidence that, considering the importance of sporadic acquisition, e.g. endogenous selection of resistant strains following antibiotic treatment, contact-isolation measures alone might not suffice to control ESBL-E. They also support the view that estimating cross-transmission extent is key to predicting the relative success of contact-isolation measures. Mathematical models could prove useful for those estimations and guide decisions concerning the most effective control strategy.