Impact of School Cycles and Environmental Forcing on the Timing of Pandemic Influenza Activity in Mexican States, May-December 2009

Integrating dynamical modeling and phylogeographic inference to characterize global influenza circulation

Global seasonal influenza circulation involves a complex interplay between local (seasonality, demography, host immunity) and global factors (international mobility) shaping recurrent epidemic patterns. No studies so far have reconciled the two spatial levels, evaluating the coupling between national epidemics, considering heterogeneous coverage of epidemiological and virological data, integrating different data sources. We propose a novel combined approach based on a dynamical model of global influenza spread (GLEAM), integrating high-resolution demographic and mobility data, and a generalized linear model of phylogeographic diffusion that accounts for time-varying migration rates. Seasonal migration fluxes across global macro-regions simulated with GLEAM are tested as phylogeographic predictors to provide model validation and calibration based on genetic data. Seasonal fluxes obtained with a specific transmissibility peak time and recurrent travel outperformed the raw air-transportation predictor, previously considered as optimal indicator of global influenza migration. Influenza A subtypes supported autumn-winter reproductive number as high as 2.25 and an average immunity duration of 2 years. Similar dynamics were preferred by influenza B lineages, with a lower autumn-winter reproductive number. Comparing simulated epidemic profiles against FluNet data offered comparatively limited resolution power. The multiscale approach enables model selection yielding a novel computational framework for describing global influenza dynamics at different scales - local transmission and national epidemics vs. international coupling through mobility and imported cases. Our findings have important implications to improve preparedness against seasonal influenza epidemics. The approach can be generalized to other epidemic contexts, such as emerging disease outbreaks to improve the flexibility and predictive power of modeling.

Mumps epidemic dynamics in the United States before vaccination (1923-1932)

Mumps is a vaccine-preventable, reemerging, and highly transmissible infectious disease. Widespread vaccination dramatically reduced cases; however, case counts have been increasing over the past 20 years. To provide a quantitative overview of historical mumps dynamics that can act as baseline information to help identify causes of mumps reemergence, we analyzed timeseries of cases reported from 1923 to 1932 in the United States. During that time, 239,230 mumps cases were reported in 70 cities. Larger cities reported annual epidemics and smaller cities reported intermittent, sporadic outbreaks. The critical community size above which transmission continuously occurred was likely between 365,583 and 781,188 individuals but could range as high as 3,376,438 individuals. Mumps cases increased as city size increased, suggesting density-dependent transmission. Using a density-dependent SEIR model, we calculated a mean effective reproductive number (Re) of 1.2. Re varied by city and over time, with periodic high values that could characterize short periods of very high transmission known as superspreading events. Case counts most often peaked in March, with higher-than-average transmission from December through April and showed a correlation with weekly births. While certain city pairs in Midwestern states had synchronous outbreaks, most outbreaks were less synchronous and not driven by distance between cities. This work demonstrates the importance of long-term infectious disease surveillance data and will inform future studies on mumps reemergence and control.

Projecting the SARS-CoV-2 transition from pandemicity to endemicity: Epidemiological and immunological considerations

In this review, we discuss the epidemiological dynamics of different viral infections to project how the transition from a pandemic to endemic Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) might take shape. Drawing from theories of disease invasion and transmission dynamics, waning immunity in the face of viral evolution and antigenic drift, and empirical data from influenza, dengue, and seasonal coronaviruses, we discuss the putative periodicity, severity, and age dynamics of SARS-CoV-2 as it becomes endemic. We review recent studies on SARS-CoV-2 epidemiology, immunology, and evolution that are particularly useful in projecting the transition to endemicity and highlight gaps that warrant further research.

New estimates of the Zika virus epidemic attack rate in Northeastern Brazil from 2015 to 2016: A modelling analysis based on Guillain-Barré Syndrome (GBS) surveillance data

BACKGROUND

Between January 2015 and August 2016, two epidemic waves of Zika virus (ZIKV) disease swept the Northeastern (NE) region of Brazil. As a result, two waves of Guillain-Barré Syndrome (GBS) were observed concurrently. The mandatory reporting of ZIKV disease began region-wide in February 2016, and it is believed that ZIKV cases were significantly under-reported before that. The changing reporting rate has made it difficult to estimate the ZIKV infection attack rate, and studies in the literature vary widely from 17% to > 50%. The same applies to other key epidemiological parameters. In contrast, the diagnosis and reporting of GBS cases were reasonably reliable given the severity and easy recognition of the disease symptoms. In this paper, we aim to estimate the real number of ZIKV cases (i.e., the infection attack rate) and their dynamics in time, by scaling up from GBS surveillance data in NE Brazil.

METHODOLOGY

A mathematical compartmental model is constructed that makes it possible to infer the true epidemic dynamics of ZIKV cases based on surveillance data of excess GBS cases. The model includes the possibility that asymptomatic ZIKV cases are infectious. The model is fitted to the GBS surveillance data and the key epidemiological parameters are inferred by using a plug-and-play likelihood-based estimation. We make use of regional weather data to determine possible climate-driven impacts on the reproductive number [Formula: see text], and to infer the true ZIKV epidemic dynamics.

FINDINGS AND CONCLUSIONS

The GBS surveillance data can be used to study ZIKV epidemics and may be appropriate when ZIKV reporting rates are not well understood. The overall infection attack rate (IAR) of ZIKV is estimated to be 24.1% (95% confidence interval: 17.1%-29.3%) of the population. By examining various asymptomatic scenarios, the IAR is likely to be lower than 33% over the two ZIKV waves. The risk rate from symptomatic ZIKV infection to develop GBS was estimated as ρ = 0.0061% (95% CI: 0.0050%-0.0086%) which is significantly less than current estimates. We found a positive association between local temperature and the basic reproduction number, [Formula: see text]. Our analysis revealed that asymptomatic infections affect the estimation of ZIKV epidemics and need to also be carefully considered in related modelling studies. According to the estimated effective reproduction number and population wide susceptibility, we comment that a ZIKV outbreak would be unlikely in NE Brazil in the near future.

Mechanistic modelling of multiple waves in an influenza epidemic or pandemic

Multiple-wave outbreaks have been documented for influenza pandemics particularly in the temperate zone, and occasionally for seasonal influenza epidemics in the tropical zone. The mechanisms shaping multiple-wave influenza outbreaks are diverse but are yet to be summarized in a systematic fashion. For this purpose, we described 12 distinct mechanistic models, among which five models were proposed for the first time, that support two waves of infection in a single influenza season, and classified them into five categories according to heterogeneities in host, pathogen, space, time and their combinations, respectively. To quantify the number of infection waves, we proposed three metrics that provide robust and intuitive results for real epidemics. Further, we performed sensitivity analyses on key parameters in each model and found that reducing the basic reproduction number or the transmission rate, limiting the addition of susceptible people who are to get the primary infection to infected areas, and limiting the probability of replenishment of people who are to be reinfected in the short term, could decrease the number of infection waves and clinical attack rate. Finally, we introduced a modelling framework to infer the mechanisms driving two-wave outbreaks. A better understanding of two-wave mechanisms could guide public health authorities to develop and implement preparedness plans and deploy control strategies.

Seasonal characteristics of influenza vary regionally across US

Given substantial regional differences in absolute humidity across the US and our understanding of the relationship between absolute humidity and influenza, we may expect important differences in regional seasonal influenza activity. Here, we assessed cross-seasonal influenza activity by comparing counts of positive influenza A and B rapid test results during the influenza season versus summer baseline periods for the 2016/2017 and 2017/2018 influenza years. Our analysis indicates significant regional patterns in cross-seasonal influenza activity, with relatively fewer influenza cases during the influenza season compared to summertime baseline periods in humid areas of the US, particularly in Florida and Hawaii. The cross-seasonal ratios vary from year-to-year and influenza type, but the geographic patterning of the ratios is relatively consistent. Mixed-effects regression models indicated absolute humidity during the influenza season was the strongest predictor of cross-seasonal influenza activity, suggesting a relationship between absolute humidity and cross-seasonal influenza activity. There was also evidence that absolute humidity during the summer plays a role, as well. This analysis suggests that spatial variation in seasonal absolute humidity levels may generate important regional differences in seasonal influenza activity and dynamics in the US.

Spatiotemporal Patterns and Diffusion of the 1918 Influenza Pandemic in British India

The factors that drive spatial heterogeneity and diffusion of pandemic influenza remain debated. We characterized the spatiotemporal mortality patterns of the 1918 influenza pandemic in British India and studied the role of demographic factors, environmental variables, and mobility processes on the observed patterns of spread. Fever-related and all-cause excess mortality data across 206 districts in India from January 1916 to December 1920 were analyzed while controlling for variation in seasonality particular to India. Aspects of the 1918 autumn wave in India matched signature features of influenza pandemics, with high disease burden among young adults, (moderate) spatial heterogeneity in burden, and highly synchronized outbreaks across the country deviating from annual seasonality. Importantly, we found population density and rainfall explained the spatial variation in excess mortality, and long-distance travel via railroad was predictive of the observed spatial diffusion of disease. A spatiotemporal analysis of mortality patterns during the 1918 influenza pandemic in India was integrated in this study with data on underlying factors and processes to reveal transmission mechanisms in a large, intensely connected setting with significant climatic variability. The characterization of such heterogeneity during historical pandemics is crucial to prepare for future pandemics.

Influenza transmission during extreme indoor conditions in a low-resource tropical setting

Influenza transmission occurs throughout the planet across wide-ranging environmental conditions. However, our understanding of the environmental factors mediating transmission is evaluated using outdoor environmental measurements, which may not be representative of the indoor conditions where influenza is transmitted. In this study, we examined the relationship between indoor environment and influenza transmission in a low-resource tropical population. We used a case-based ascertainment design to enroll 34 households with a suspected influenza case and then monitored households for influenza, while recording indoor temperature and humidity data in each household. We show that the indoor environment is not commensurate with outdoor conditions and that the relationship between indoor and outdoor conditions varies significantly across homes. We also show evidence of influenza transmission in extreme indoor environments. Specifically, our data suggests that indoor environments averaged 29 °C, 18 g/kg specific humidity, and 68 % relative humidity across 15 transmission events observed. These indoor settings also exhibited significant temporal variability with temperatures as high as 39 °C and specific and relative humidity increasing to 22 g/kg and 85 %, respectively, during some transmission events. However, we were unable to detect differences in the transmission efficiency by indoor temperature or humidity conditions. Overall, these results indicate that laboratory studies investigating influenza transmission and virus survival should increase the range of environmental conditions that they assess and that observational studies investigating the relationship between environment and influenza activity should use caution using outdoor environmental measurements since they can be imprecise estimates of the conditions that mediate transmission indoors.

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.

The underlying mechanisms dictating the spatial spread of seasonal influenza remain poorly understood, in part because of the lack of spatially resolved disease data to quantify patterns of spread. In this paper, we address this issue by analyzing fine-grain insurance claims data on influenza-like-illnesses over eight seasons in ~300 locations throughout the United States. Using statistical methods, we found that seven of eight epidemics likely originated in the Southern US, that influenza spatial transmission is dominated by local traffic between cities, and that seasons marked by novel influenza virus circulation had a particularly radial, localized spatial structure. These findings are in stark contrast to prevailing theories of influenza spatial transmission that suggest that transmission is favored in low humidity environments and that spread is a dominated by air traffic between populous hubs.

National Influenza Surveillance in the Philippines from 2006 to 2012: seasonality and circulating strains

Background

The results of routine influenza surveillance in 13 regions in the Philippines from 2006 to 2012 are presented, describing the annual seasonal epidemics of confirmed influenza virus infection, seasonal and alert thresholds, epidemic curve, and circulating influenza strains.

Methods

Retrospective analysis of Philippine influenza surveillance data from 2006 to 2012 was conducted to determine seasonality with the use of weekly influenza positivity rates and calculating epidemic curves and seasonal and alert thresholds using the World Health Organization (WHO) global epidemiological surveillance standards for influenza.

Results

Increased weekly influenza positive rates were observed from June to November, coinciding with the rainy season and school opening. Two or more peaks of influenza activity were observed with different dominant influenza types associated with each peak. A-H1N1, A-H3N2, and two types of B viruses circulated during the influenza season in varying proportions every year. Increased influenza activity for 2012 occurred 8 weeks late in week 29, rather than the expected week of rise of cases in week 21 as depicted in the established average epidemic curve and seasonal threshold. The intensity was severe going above the alert threshold but of short duration. Southern Hemisphere vaccine strains matched circulating influenza virus for more surveillance years than Northern Hemisphere vaccine strains.

Conclusions

Influenza seasonality in the Philippines is from June to November. The ideal time to administer Southern Hemisphere influenza vaccine should be from April to May. With two lineages of influenza B circulating annually, quadrivalent vaccine might have more impact on influenza control than trivalent vaccine. Establishment of thresholds and average epidemic curve provide a tool for policy-makers to assess the intensity or severity of the current influenza epidemic even early in its course, to help plan more precisely resources necessary to control the outbreak. Influenza surveillance activities should be continued in the Philippines and funding for such activities should already be incorporated into the Philippine health budget.

Electronic supplementary material

The online version of this article (doi:10.1186/s12879-016-2087-9) contains supplementary material, which is available to authorized users.

Model-based reconstruction of an epidemic using multiple datasets: understanding influenza A/H1N1 pandemic dynamics in Israel

Intensified surveillance during the 2009 A/H1N1 influenza pandemic in Israel resulted in large virological and serological datasets, presenting a unique opportunity for investigating the pandemic dynamics. We employ a conditional likelihood approach for fitting a disease transmission model to virological and serological data, conditional on clinical data. The model is used to reconstruct the temporal pattern of the pandemic in Israel in five age-groups and evaluate the factors that shaped it. We estimate the reproductive number at the beginning of the pandemic to beR= 1.4. We find that the combined effect of varying absolute humidity conditions and school vacations (SVs) is responsible for the infection pattern, characterized by three epidemic waves. Overall attack rate is estimated at 32% (28-35%) with a large variation among the age-groups: the highest attack rates within school children and the lowest within the elderly. This pattern of infection is explained by a combination of the age-group contact structure and increasing immunity with age. We assess that SVs increased the overall attack rates by prolonging the pandemic into the winter. Vaccinating school children would have been the optimal strategy for minimizing infection rates in all age-groups.

Mathematical models to characterize early epidemic growth: A review

There is a long tradition of using mathematical models to generate insights into the transmission dynamics of infectious diseases and assess the potential impact of different intervention strategies. The increasing use of mathematical models for epidemic forecasting has highlighted the importance of designing reliable models that capture the baseline transmission characteristics of specific pathogens and social contexts. More refined models are needed however, in particular to account for variation in the early growth dynamics of real epidemics and to gain a better understanding of the mechanisms at play. Here, we review recent progress on modeling and characterizing early epidemic growth patterns from infectious disease outbreak data, and survey the types of mathematical formulations that are most useful for capturing a diversity of early epidemic growth profiles, ranging from sub-exponential to exponential growth dynamics. Specifically, we review mathematical models that incorporate spatial details or realistic population mixing structures, including meta-population models, individual-based network models, and simple SIR-type models that incorporate the effects of reactive behavior changes or inhomogeneous mixing. In this process, we also analyze simulation data stemming from detailed large-scale agent-based models previously designed and calibrated to study how realistic social networks and disease transmission characteristics shape early epidemic growth patterns, general transmission dynamics, and control of international disease emergencies such as the 2009 A/H1N1 influenza pandemic and the 2014-2015 Ebola epidemic in West Africa.

An Epidemiological Analysis of Summer Influenza Epidemics in Okinawa

Objective This study evaluates the difference between winter influenza and summer influenza in Okinawa. Methods From January 2007 to June 2014, weekly rapid antigen test (RAT) results performed in four acute care hospitals were collected for the surveillance of regional influenza prevalence in the Naha region of the Okinawa Islands. Results An antigenic data analysis revealed that multiple H1N1 and H3N2 viruses consistently co-circulate in Okinawa, creating synchronized seasonal patterns and a high genetic diversity of influenza A. Additionally, influenza B viruses play a significant role in summer epidemics, almost every year. To further understand influenza epidemics during the summer in Okinawa, we evaluated the full genome sequences of some representative human influenza A and influenza B viruses isolated in Okinawa. Phylogenetic data analysis also revealed that multiple H1N1 and H3N2 viruses consistently co-circulate in Okinawa. Conclusion This surveillance revealed a distinct epidemic pattern of seasonal and pandemic influenza in this subtropical region.