Host heterogeneity and epistasis explain punctuated evolution of SARS-CoV-2.

Identifying drivers of viral diversity is key to understanding the evolutionary as well as epidemiological dynamics of the COVID-19 pandemic. Using rich viral genomic data sets, we show that periods of steadily rising diversity have been punctuated by sudden, enormous increases followed by similarly abrupt collapses of diversity. We introduce a mechanistic model of saltational evolution with epistasis and demonstrate that these features parsimoniously account for the observed temporal dynamics of inter-genomic diversity. Our results provide support for recent proposals that saltational evolution may be a signature feature of SARS-CoV-2, allowing the pathogen to more readily evolve highly transmissible variants. These findings lend theoretical support to a heightened awareness of biological contexts where increased diversification may occur. They also underline the power of pathogen genomics and other surveillance streams in clarifying the phylodynamics of emerging and endemic infections. In public health terms, our results further underline the importance of equitable distribution of up-to-date vaccines.

Overdispersion in COVID-19 increases the effectiveness of limiting nonrepetitive contacts for transmission control.

Increasing evidence indicates that superspreading plays a dominant role in COVID-19 transmission. Recent estimates suggest that the dispersion parameter k for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is on the order of 0.1, which corresponds to about 10% of cases being the source of 80% of infections. To investigate how overdispersion might affect the outcome of various mitigation strategies, we developed an agent-based model with a social network that allows transmission through contact in three sectors: "close" (a small, unchanging group of mutual contacts as might be found in a household), "regular" (a larger, unchanging group as might be found in a workplace or school), and "random" (drawn from the entire model population and not repeated regularly). We assigned individual infectivity from a gamma distribution with dispersion parameter k We found that when k was low (i.e., greater heterogeneity, more superspreading events), reducing random sector contacts had a far greater impact on the epidemic trajectory than did reducing regular contacts; when k was high (i.e., less heterogeneity, no superspreading events), that difference disappeared. These results suggest that overdispersion of COVID-19 transmission gives the virus an Achilles' heel: Reducing contacts between people who do not regularly meet would substantially reduce the pandemic, while reducing repeated contacts in defined social groups would be less effective.

A Retrospective Cohort Study on Infant Respiratory Tract Infection Hospitalizations and Recurrent Wheeze and Asthma Risk: Impact of Respiratory Syncytial Virus.

BACKGROUND: Infant respiratory syncytial virus infection (RSV) has been associated with asthma later in life. We explored the risk of recurrent wheeze or asthma in children with infant RSV-associated hospitalization compared to other respiratory infections. METHODS: We performed a retrospective cohort study using Danish national hospital discharge registers. Infants younger than 6 months, born between January 1995 and October 2018, and with a RSV hospital admission were compared to infants hospitalized for injuries, non-RSV acute upper respiratory tract infection (AURTI), pneumonia and other respiratory pathogens, nonpathogen-coded lower respiratory tract infections (LRTI), pertussis, or nonspecific respiratory infections. Infants were followed until recurrent wheeze or asthma diagnosis, death, migration, age 10 years, or study end. We estimated cumulative incidence rate ratios (CIRR) and hazard ratios (HR) adjusted for sex, age at inclusion, hospital length of stay (LOS), maternal smoking, 5-minute APGAR score (APGAR5), prematurity, and congenital risk factors (CRF). RESULTS: We included 68 130 infants, of whom 20 920 (30.7%) had RSV hospitalization. The cumulative incidence rate of recurrent wheeze or asthma was 16.6 per 1000 person-years after RSV hospitalization, higher than after injury (CIRR, 2.69; 95% confidence interval [CI], 2.48-2.92), AURTI (CIRR, 1.48; 95% CI, 1.34-1.58), or pertussis (CIRR, 2.32; 95% CI, 1.85-2.91), similar to pneumonia and other respiratory pathogens (CIRR, 1.15; 95% CI, .99-1.34) and LRTI (CIRR, 0.79; 95% CI, .60-1.04), but lower than nonspecific respiratory infections (CIRR, 0.79; 95% CI, .73-.87). Adjusted HRs for recurrent wheeze or asthma after RSV hospitalization compared to injuries decreased from 2.37 (95% CI, 2.08-2.70) for 0 to <1 year to 1.23 (95% CI, .88-1.73) for 6 to <10 years for term-born children, and from 1.48 (95% CI, 1.09-2.00) to 0.60 (95% CI, .25-1.43) for preterm-born children. Sex, maternal smoking, LOS, CRF, and APGAR5 were independent risk factors. CONCLUSIONS: Infant RSV hospitalization is associated with recurrent wheeze and asthma hospitalization, predominantly at preschool age. If causal, RSV prophylaxis, including vaccines, may significantly reduce disease burden of wheeze and asthma.

Temperate climate malaria in nineteenth century Denmark.

BACKGROUND: Plasmodium vivax was endemic in northern Europe until the early twentieth century. Considering climate change and the recent emergence of other vector borne diseases in Europe, historical insight into the relationship between malaria and environmental factors in northern Europe is needed. This article describes malaria epidemiology in late-nineteenth century Denmark. METHODS: We described the seasonality and spatial patterns of malaria, and the relationship of the disease with environmental factors such as soil types, clay content and elevation for the period 1862-1914. We studied demographic and seasonal patterns and malaria mortality in the high-morbidity period of 1862-1880. Finally, we studied the relationship between malaria seasonality and temperature and precipitation using a Spearman correlation test. RESULTS: We found that the highest incidence occurred in eastern Denmark. Lolland-Falster medical region experienced the highest incidence (14.5 cases per 1000 pop.) and Bornholm medical region experienced the lowest incidence (0.57 cases per 1000 pop.). Areas with high malaria incidence also had high soil clay content, high agricultural production, and Lolland-Falster furthermore has a low elevation. Malaria incidence typically peaked in May and was associated with high temperatures in July and August of the previous year but not with precipitation. The case fatality rate was 0.17%, and the disease affected both sexes and all age groups except for infants. In 1873, a large epidemic occurred following flooding from a storm surge in November 1872. CONCLUSIONS: Malaria gradually declined in Denmark during our study period and had essentially disappeared by 1900. The high adult and low child morbidity in 1862-1880 indicates that malaria was not highly endemic in this period, as malaria is most frequent among children in highly endemic areas today. The association of high malaria incidence in spring with warmer temperatures in the previous summer suggests that transmission took place in the previous summers. The close geographical connection between malaria and soil types, agricultural production and elevation suggests that these factors are detrimental to sustain endemic malaria. Our findings of a close connection between malaria and environmental factors such as climate and geography provides insights to address potential reintroduction of malaria in temperate climates.

Seroprevalence of SARS-CoV-2 antibodies in social housing areas in Denmark.

BACKGROUND: COVID-19 is thought to be more prevalent among ethnic minorities and individuals with low socioeconomic status. We aimed to investigate the prevalence of SARS-CoV-2 antibodies during the COVID-19 pandemic among citizens 15 years or older in Denmark living in social housing (SH) areas. METHODS: We conducted a study between January 8th and January 31st, 2021 with recruitment in 13 selected SH areas. Participants were offered a point-of-care rapid SARS-CoV-2 IgM and IgG antibody test and a questionnaire concerning risk factors associated with COVID-19. As a proxy for the general Danish population we accessed data on seroprevalence from Danish blood donors (total Ig ELISA assay) in same time period. RESULTS: Of the 13,279 included participants, 2296 (17.3%) were seropositive (mean age 46.6 (SD 16.4) years, 54.2% female), which was 3 times higher than in the general Danish population (mean age 41.7 (SD 14.1) years, 48.5% female) in the same period (5.8%, risk ratios (RR) 2.96, 95% CI 2.78-3.16, p > 0.001). Seropositivity was higher among males (RR 1.1, 95% CI 1.05-1.22%, p = 0.001) and increased with age, with an OR seropositivity of 1.03 for each 10-year increase in age (95% CI 1.00-1.06, p = 0.031). Close contact with COVID-19-infected individuals was associated with a higher risk of infection, especially among household members (OR 5.0, 95% CI 4.1-6.2 p < 0,001). Living at least four people in a household significantly increased the OR of seropositivity (OR 1.3, 95% CI 1.0-1.6, p = 0.02) as did living in a multi-generational household (OR 1.3 per generation, 95% CI 1.1-1.6, p = 0.003). Only 1.6% of participants reported not following any of the national COVID-19 recommendations. CONCLUSIONS: Danish citizens living in SH areas of low socioeconomic status had a three times higher SARS-CoV-2 seroprevalence compared to the general Danish population. The seroprevalence was significantly higher in males and increased slightly with age. Living in multiple generations households or in households of more than four persons was a strong risk factor for being seropositive. Results of this study can be used for future consideration of the need for preventive measures in the populations living in SH areas.

SARS-CoV-2 antibody prevalence among homeless people and shelter workers in Denmark: a nationwide cross-sectional study.

BACKGROUND: People experiencing homelessness (PEH) and associated shelter workers may be at higher risk of infection with "Severe acute respiratory syndrome coronavirus 2" (SARS-CoV-2). The aim of this study was to determine the prevalence of SARS-CoV-2 among PEH and shelter workers in Denmark. DESIGN AND METHODS: In November 2020, we conducted a nationwide cross-sectional seroprevalence study among PEH and shelter workers at 21 recruitment sites in Denmark. The assessment included a point-of-care test for antibodies against SARS-CoV-2, followed by a questionnaire. The seroprevalence was compared to that of geographically matched blood donors considered as a proxy for the background population, tested using a total Ig ELISA assay. RESULTS: We included 827 participants in the study, of whom 819 provided their SARS-CoV-2 antibody results. Of those, 628 were PEH (median age 50.8 (IQR 40.9-59.1) years, 35.5% female) and 191 were shelter workers (median age 46.6 (IQR 36.1-55.0) years and 74.5% female). The overall seroprevalence was 6.7% and was similar among PEH and shelter workers (6.8% vs 6.3%, p = 0.87); and 12.2% among all participants who engaged in sex work. The overall participant seroprevalence was significantly higher than that of the background population (2.9%, p < 0.001). When combining all participants who reported sex work or were recruited at designated safe havens, we found a significantly increased risk of seropositivity compared to other participants (OR 2.23, 95%CI 1.06-4.43, p = 0.02). Seropositive and seronegative participants reported a similar presence of at least one SARS-CoV-2 associated symptom (49% and 54%, respectively). INTERPRETATIONS: The prevalence of SARS-CoV-2 antibodies was more than twice as high among PEH and associated shelter workers, compared to the background population. These results could be taken into consideration when deciding in which phase PEH are eligible for a vaccine, as part of the Danish national SARS-CoV-2 vaccination program rollout. FUNDING: TrygFonden and HelseFonden.

COVID-19 Superspreading Suggests Mitigation by Social Network Modulation.

Although COVID-19 has caused severe suffering globally, the efficacy of nonpharmaceutical interventions has been greater than typical models have predicted. Meanwhile, evidence is mounting that the pandemic is characterized by superspreading. Capturing this phenomenon theoretically requires modeling at the scale of individuals. Using a mathematical model, we show that superspreading drastically enhances mitigations which reduce the overall personal contact number and that social clustering increases this effect.

Doubling Time of the COVID-19 Epidemic by Province, China.

In China, the doubling time of the coronavirus disease epidemic by province increased during January 20-February 9, 2020. Doubling time estimates ranged from 1.4 (95% CI 1.2-2.0) days for Hunan Province to 3.1 (95% CI 2.1-4.8) days for Xinjiang Province. The estimate for Hubei Province was 2.5 (95% CI 2.4-2.6) days.

Estimating the population-level impact of vaccines using synthetic controls.

When a new vaccine is introduced, it is critical to monitor trends in disease rates to ensure that the vaccine is effective and to quantify its impact. However, estimates from observational studies can be confounded by unrelated changes in healthcare utilization, changes in the underlying health of the population, or changes in reporting. Other diseases are often used to detect and adjust for these changes, but choosing an appropriate control disease a priori is a major challenge. The "synthetic controls" (causal impact) method, which was originally developed for website analytics and social sciences, provides an appealing solution. With this approach, potential comparison time series are combined into a composite and are used to generate a counterfactual estimate, which can be compared with the time series of interest after the intervention. We sought to estimate changes in hospitalizations for all-cause pneumonia associated with the introduction of pneumococcal conjugate vaccines (PCVs) in five countries in the Americas. Using synthetic controls, we found a substantial decline in hospitalizations for all-cause pneumonia in infants in all five countries (average of 20%), whereas estimates for young and middle-aged adults varied by country and were potentially influenced by the 2009 influenza pandemic. In contrast to previous reports, we did not detect a decline in all-cause pneumonia in older adults in any country. Synthetic controls promise to increase the accuracy of studies of vaccine impact and to increase comparability of results between populations compared with alternative approaches.

Challenges in Estimating the Impact of Vaccination with Sparse Data.

BACKGROUND: The synthetic control model is a powerful tool to quantify the population-level impact of vaccines because it can adjust for trends unrelated to vaccination using a composite of control diseases. Because vaccine impact studies are often conducted using smaller, subnational datasets, we evaluated the performance of synthetic control models with sparse time series data. To obtain more robust estimates of vaccine impacts from noisy time series, we proposed a possible alternative approach, STL+PCA method (seasonal-trend decomposition plus principal component analysis), which first extracts smoothed trends from the control time series and uses them to adjust the outcome. METHODS: Using both the synthetic control and STL+PCA models, we estimated the impact of 10-valent pneumococcal conjugate vaccine on pneumonia hospitalizations among cases <12 months and 80+ years of age during 2004-2014 at the subnational level in Brazil. We compared the performance of these models using simulation analyses. RESULTS: The synthetic control model was able to adjust for trends unrelated to 10-valent pneumococcal conjugate vaccine in larger states but not in smaller states. Simulation analyses showed that the estimates obtained with the synthetic control approach were biased when there were fewer cases, and only 4% of simulations had credible intervals covering the true estimate. In contrast, the STL+PCA analysis had 90% lower bias and had 95% of simulations, with credible intervals covering the true estimate. CONCLUSIONS: Estimates from the synthetic control model might be biased when data are sparse. The STL+PCA model provides more accurate evaluations of vaccine impact in smaller populations.

Individual and household exposures associated with cholera transmission in case-control studies: a systematic review.

OBJECTIVE: Uncertainty persists regarding cholera transmission routes. We conducted a structured review of case-control studies on cholera transmission and provide a qualitative summary of reported exposures in order to inform public health efforts and future cholera research. METHODS: We searched two electronic databases for published case-control studies that investigated risk factors for cholera and included any publications that did not match our exclusion criteria. From the included studies, we grouped exposures using two parameters, whether transmission domain was public or domestic, and also on the vehicle of transmission. We extracted data on study location, method of case and control inclusion, type of statistical analysis performed and which exposures were included. Additionally, two parallel subgroup analyses were performed. The first included the subgroup of all studies that used culture-confirmed cholera cases, and the second included the subgroup of all studies employing a multivariate analysis. In the second analysis, we calculated the population attributable risk (PAR). RESULTS: Our search yielded 2347 peer-reviewed publications, of which 65 did not match our exclusion criteria, comprising 69 individual studies. Water-based exposures were investigated in 97% of these studies, of which 70% found a significant association with cholera infection. Food-based exposures were investigated in 75% of studies, of which 63% found a significant association with risk of cholera infection. Close personal contact with cholera cases was investigated in 30% of studies, of which 52% found a significant association with risk of cholera infection. Hygiene-related exposures were investigated in 51% of studies, of which 63% found a significant association with cholera transmission. Among studies that examined at least one exposure related to the domestic domain, 76% found a significant association with cholera infection vs. 71% of studies investigating at least one public domain exposure. The subgroup analyses produced similar results. CONCLUSIONS: Despite strong evidence for cholera transmission via foodborne-, hygiene-, waterborne- and close personal contact-related pathways in both domestic and public domains, we found that non-waterborne-related factors are understudied. Future cholera case-control studies would benefit from investigating all transmission vehicles and transmission domains.

OBJECTIF: L'incertitude persiste quant aux voies de transmission du choléra. Nous avons mené une analyse structurée d'études cas-témoins sur la transmission du choléra et fourni un résumé qualitatif des expositions rapportées afin d'éclairer les efforts de santé publique et les recherches futures sur le choléra. MÉTHODES: Nous avons recherché dans deux bases de données électroniques, des études cas-témoins publiées, portant sur les facteurs de risque du choléra et avons inclus toutes les publications ne correspondant pas à nos critères d'exclusion. Parmi les études incluses, nous avons regroupé les expositions en utilisant deux paramètres, selon que le domaine de transmission soit public ou domestique, ainsi que sur le véhicule de transmission. Nous avons extrait les données sur le lieu de l'étude, la méthode d'inclusion des cas et des témoins, le type d'analyse statistique effectuée et les expositions incluses. En outre, deux analyses parallèles de sous-groupes ont été effectuées. La première comprenait le sous-groupe de toutes les études utilisant des cas de choléra confirmés par culture et la seconde, le sous-groupe de toutes les études utilisant une analyse multivariée. Dans la seconde analyse, nous avons calculé le risque attribuable à la population. RÉSULTATS: Notre recherche a donné 2347 publications revues par des pairs, dont 65 ne correspondaient pas à nos critères d'exclusion et comprenant 69 études individuelles. Les expositions à base d'eau ont été investiguées dans 97% de ces études, parmi lesquelles 70% ont trouvé une association significative avec l'infection par le choléra. Les expositions d'origine alimentaire ont été investiguées dans 75% des études, dont 63% ont montré une association significative avec le risque d'infection par le choléra. Le contact personnel rapproché avec des cas de choléra a été investigué dans 30% des études, parmi lesquelles 52% ont trouvé une association significative avec le risque d'infection par le choléra. Les expositions liées à l'hygiène ont été étudiées dans 51% des études, dont 63% ont montré une association significative avec la transmission du choléra. Parmi les études portant sur au moins une exposition liée au domaine domestique, 76% ont trouvé une association significative avec l'infection par le choléra, contre 71% des études portant sur au moins une exposition du domaine public. Les analyses de sous-groupes ont produit des résultats similaires. CONCLUSIONS: Malgré de solides preuves sur la transmission du choléra par des voies d'origine alimentaire, hygiénique, d'origine hydrique et de contact rapproché, dans les domaines domestique et public, nous avons constaté que les facteurs non liés à l'eau étaient peu étudiés. Les futures études cas-témoins sur le choléra gagneraient à investiguer sur tous les véhicules et tous les domaines de transmission.

Cholera Epidemics of the Past Offer New Insights Into an Old Enemy.

Background: Although cholera is considered the quintessential long-cycle waterborne disease, studies have emphasized the existence of short-cycle (food, household) transmission. We investigated singular Danish cholera epidemics (in 1853) to elucidate epidemiological parameters and modes of spread. Methods: Using time series data from cities with different water systems, we estimated the intrinsic transmissibility (R0). Accessing cause-specific mortality data, we studied clinical severity and age-specific impact. From physicians' narratives we established transmission chains and estimated serial intervals. Results: Epidemics were seeded by travelers from cholera-affected cities; initial transmission chains involving household members and caretakers ensued. Cholera killed 3.4%-8.9% of the populations, with highest mortality among seniors (16%) and lowest in children (2.7%). Transmissibility (R0) was 1.7-2.6 and the serial interval was estimated at 3.7 days (95% confidence interval, 2.9-4.7 days). The case fatality ratio (CFR) was high (54%-68%); using R0 we computed an adjusted CFR of 4%-5%. Conclusions: Short-cycle transmission was likely critical to early secondary transmission in historic Danish towns. The outbreaks resembled the contemporary Haiti outbreak with respect to transmissibility, age patterns, and CFR, suggesting a role for broader hygiene/sanitation interventions to control contemporary outbreaks.

Using Clinical Research Networks to Assess Severity of an Emerging Influenza Pandemic.

Background: Early clinical severity assessments during the 2009 influenza A H1N1 pandemic (pH1N1) overestimated clinical severity due to selection bias and other factors. We retrospectively investigated how to use data from the International Network for Strategic Initiatives in Global HIV Trials, a global clinical influenza research network, to make more accurate case fatality ratio (CFR) estimates early in a future pandemic, an essential part of pandemic response. Methods: We estimated the CFR of medically attended influenza (CFRMA) as the product of probability of hospitalization given confirmed outpatient influenza and the probability of death given hospitalization with confirmed influenza for the pandemic (2009-2011) and post-pandemic (2012-2015) periods. We used literature survey results on health-seeking behavior to convert that estimate to CFR among all infected persons (CFRAR). Results: During the pandemic period, 5.0% (3.1%-6.9%) of 561 pH1N1-positive outpatients were hospitalized. Of 282 pH1N1-positive inpatients, 8.5% (5.7%-12.6%) died. CFRMA for pH1N1 was 0.4% (0.2%-0.6%) in the pandemic period 2009-2011 but declined 5-fold in young adults during the post-pandemic period compared to the level of seasonal influenza in the post-pandemic period 2012-2015. CFR for influenza-negative patients did not change over time. We estimated the 2009 pandemic CFRAR to be 0.025%, 16-fold lower than CFRMA. Conclusions: Data from a clinical research network yielded accurate pandemic severity estimates, including increased severity among younger people. Going forward, clinical research networks with a global presence and standardized protocols would substantially aid rapid assessment of clinical severity. Clinical Trials Registration: NCT01056354 and NCT01056185

Loose Ends in the Epidemiology of the 1918 Pandemic: Explaining the Extreme Mortality Risk in Young Adults.

In the century since the 1918 influenza pandemic, insights have been sought to explain the pandemic's signature pattern of high death rates in young adults and low death rates in the elderly and infants. Our understanding of the origin and evolution of the pandemic has shifted considerably. We review evidence of the characteristic age-related pattern of death during the 1918 pandemic relative to the "original antigenic sin" hypothesis. We analyze age-stratified mortality data from Copenhagen around 1918 to identify break points associated with unusual death risk. Whereas infants had no meaningful risk elevation, death risk gradually increased, peaking for young adults 20-34 years of age before dropping sharply for adults ages 35-44 years, suggesting break points for birth cohorts around 1908 and 1878. Taken together with data from previous studies, there is strong evidence that those born before 1878 or after 1908 were not at increased risk of dying of 1918 pandemic influenza. Although the peak death risk coincided with the 1889-1892 pandemic, the 1908 and 1878 break points do not correspond with known pandemics. An increasing number of interdisciplinary studies covering fields such as virology, phylogenetics, death, and serology offer exciting insights into patterns and reasons for the unusual extreme 1918 pandemic mortality risk in young adults.

Human mobility and the spatial transmission of influenza in the United States.

Seasonal influenza epidemics offer unique opportunities to study the invasion and re-invasion waves of a pathogen in a partially immune population. Detailed patterns of spread remain elusive, however, due to lack of granular disease data. Here we model high-volume city-level medical claims data and human mobility proxies to explore the drivers of influenza spread in the US during 2002-2010. Although the speed and pathways of spread varied across seasons, seven of eight epidemics likely originated in the Southern US. Each epidemic was associated with 1-5 early long-range transmission events, half of which sparked onward transmission. Gravity model estimates indicate a sharp decay in influenza transmission with the distance between infectious and susceptible cities, consistent with spread dominated by work commutes rather than air traffic. Two early-onset seasons associated with antigenic novelty had particularly localized modes of spread, suggesting that novel strains may spread in a more localized fashion than previously anticipated.

Impact of Pneumococcal Conjugate Vaccines on Pneumonia Hospitalizations in High- and Low-Income Subpopulations in Brazil.

BACKGROUND: Pneumococcal conjugate vaccines (PCVs) are being used worldwide. A key question is whether the impact of PCVs on pneumonia is similar in low- and high-income populations. However, most low-income countries, where the burden of disease is greatest, lack reliable data that can be used to evaluate the impact. Data from middle-income countries that have both low- and high-income subpopulations can provide a proxy measure for the impact of the vaccine in low-income countries. METHODS: We evaluated the impact of PCV10 on hospitalizations for all-cause pneumonia in Brazil, a middle-income country with localities that span a broad range of human development index (HDI) levels. We used complementary time series and spatiotemporal methods (synthetic controls and hierarchical Bayesian spatial regression) to test whether the decline in pneumonia hospitalizations associated with vaccine introduction varied across the socioeconomic spectrum. RESULTS: We found that the declines in all-cause pneumonia hospitalizations in children and young and middle-aged adults did not vary substantially across low and high HDI subpopulations. Moreover, the estimated declines seen in infants and young adults were associated with higher levels of uptake of the vaccine at a local level. CONCLUSIONS: These results suggest that PCVs have an important impact on hospitalizations for all-cause pneumonia in both low- and high-income populations.

Perspectives on model forecasts of the 2014-2015 Ebola epidemic in West Africa: lessons and the way forward.

The unprecedented impact and modeling efforts associated with the 2014-2015 Ebola epidemic in West Africa provides a unique opportunity to document the performances and caveats of forecasting approaches used in near-real time for generating evidence and to guide policy. A number of international academic groups have developed and parameterized mathematical models of disease spread to forecast the trajectory of the outbreak. These modeling efforts often relied on limited epidemiological data to derive key transmission and severity parameters, which are needed to calibrate mechanistic models. Here, we provide a perspective on some of the challenges and lessons drawn from these efforts, focusing on (1) data availability and accuracy of early forecasts; (2) the ability of different models to capture the profile of early growth dynamics in local outbreaks and the importance of reactive behavior changes and case clustering; (3) challenges in forecasting the long-term epidemic impact very early in the outbreak; and (4) ways to move forward. We conclude that rapid availability of aggregated population-level data and detailed information on a subset of transmission chains is crucial to characterize transmission patterns, while ensemble-forecasting approaches could limit the uncertainty of any individual model. We believe that coordinated forecasting efforts, combined with rapid dissemination of disease predictions and underlying epidemiological data in shared online platforms, will be critical in optimizing the response to current and future infectious disease emergencies.

Bayesian Model Averaging with Change Points to Assess the Impact of Vaccination and Public Health Interventions.

BACKGROUND: Pneumococcal conjugate vaccines (PCVs) prevent invasive pneumococcal disease and pneumonia. However, some low-and middle-income countries have yet to introduce PCV into their immunization programs due, in part, to lack of certainty about the potential impact. Assessing PCV benefits is challenging because specific data on pneumococcal disease are often lacking, and it can be difficult to separate the effects of factors other than the vaccine that could also affect pneumococcal disease rates. METHODS: We assess PCV impact by combining Bayesian model averaging with change-point models to estimate the timing and magnitude of vaccine-associated changes, while controlling for seasonality and other covariates. We applied our approach to monthly time series of age-stratified hospitalizations related to pneumococcal infection in children younger 5 years of age in the United States, Brazil, and Chile. RESULTS: Our method accurately detected changes in data in which we knew true and noteworthy changes occurred, i.e., in simulated data and for invasive pneumococcal disease. Moreover, 24 months after the vaccine introduction, we detected reductions of 14%, 9%, and 9% in the United States, Brazil, and Chile, respectively, in all-cause pneumonia (ACP) hospitalizations for age group 0 to <1 years of age. CONCLUSIONS: Our approach provides a flexible and sensitive method to detect changes in disease incidence that occur after the introduction of a vaccine or other intervention, while avoiding biases that exist in current approaches to time-trend analyses.

Big Data for Infectious Disease Surveillance and Modeling.

We devote a special issue of the Journal of Infectious Diseases to review the recent advances of big data in strengthening disease surveillance, monitoring medical adverse events, informing transmission models, and tracking patient sentiments and mobility. We consider a broad definition of big data for public health, one encompassing patient information gathered from high-volume electronic health records and participatory surveillance systems, as well as mining of digital traces such as social media, Internet searches, and cell-phone logs. We introduce nine independent contributions to this special issue and highlight several cross-cutting areas that require further research, including representativeness, biases, volatility, and validation, and the need for robust statistical and hypotheses-driven analyses. Overall, we are optimistic that the big-data revolution will vastly improve the granularity and timeliness of available epidemiological information, with hybrid systems augmenting rather than supplanting traditional surveillance systems, and better prospects for accurate infectious diseases models and forecasts.