Rural-urban gradient in seasonal forcing of measles transmission in Niger

Descriptive Analysis of Measles Outbreak in Liberia, 2022

Background

Liberia reported a large outbreak of measles involving all the counties in 2022. We conducted a descriptive analysis of the measles surveillance data to understand the trend of the outbreak and guide further policy action to prevent future outbreaks.

Methods

We analyzed the measles surveillance data from Epi week 1 to 51, 2022. All the laboratory-confirmed cases, clinically compatible and epidemiologically linked cases were included in the analysis, the variables of interest included the patient's age, sex, place of residence, measles classification, measles vaccination status, and outcome. We cleaned and analyzed the data using R version 4.2.0 and Arc GIS Pro. The demographic characteristics of the cases were presented, the progression of the cases was presented in Epicurve and the spatial distribution and the case fatality rate (CFR) of the case were presented at the district level using the Arc GIS Pro.

Results

The median age of the cases was 4 years (interquartile range: 2-8 years). Children under five years of age constituted 60% of the cases (4836/8127), and females accounted for 52% (4204/8127) of the cases. Only 1% (84/8127) of the cases had documentary evidence of receiving at least one dose of measles-containing vaccine (MCV). Only 3 out of 92 health districts in the country did not report a case of measles during the period under review. The overall cases fatality rate was 1% however CFR of up to 10% were reported in some districts.

Conclusion

The outbreak of measles involved almost all the districts of the country, exposing a possible nationwide suboptimal immunization coverage for MCV. The high CFR reported in some districts needs further investigation.

Estimation of the relationship between meteorological factors and measles using spatiotemporal Bayesian model in Shandong Province, China

BACKGROUND

Measles-containing vaccine (MCV) has been effective in controlling the spread of measles. Some countries have declared measles elimination. But recently years, the number of cases worldwide has increased, posing a challenge to the global goal of measles eradication. This study estimated the relationship between meteorological factors and measles using spatiotemporal Bayesian model, aiming to provide scientific evidence for public health policy to eliminate measles.

METHODS

Descriptive statistical analysis was performed on monthly data of measles and meteorological variables in 136 counties of Shandong Province from 2009 to 2017. Spatiotemporal Bayesian model was used to estimate the effects of meteorological factors on measles, and to evaluate measles risk areas at county level. Case population was divided into multiple subgroups according to gender, age and occupation. The effects of meteorological factors on measles in subgroups were compared.

RESULTS

Specific meteorological conditions increased the risk of measles, including lower relative humidity, temperature, and atmospheric pressure; higher wind velocity, sunshine duration, and diurnal temperature variation. Taking lowest value (Q1) as reference, RR (95%CI) for higher temperatures (Q2-Q4) were 0.79 (0.69-0.91), 0.54 (0.44-0.65), and 0.48 (0.38-0.61), respectively; RR (95%CI) for higher relative humidity (Q2-Q4) were 0.76 (0.66-0.88), 0.56 (0.47-0.67), and 0.49 (0.38-0.63), respectively; RR (95%CI) for higher wind velocity (Q2-Q4) were 1.43 (1.25-1.64), 1.85 (1.57-2.18), 2.00 (1.59-2.52), respectively. 22 medium-to-high risk counties were identified, mainly in northwestern, southwestern and central Shandong Province. The trend was basically same in the effects of meteorological factors on measles in subgroups, but the magnitude of the effects was different.

CONCLUSIONS

Meteorological factors have an important impact on measles. It is crucial to integrate these factors into public health policies for measles prevention and control in China.

Megacities as drivers of national outbreaks: The 2017 chikungunya outbreak in Dhaka, Bangladesh

BACKGROUND

Several large outbreaks of chikungunya have been reported in the Indian Ocean region in the last decade. In 2017, an outbreak occurred in Dhaka, Bangladesh, one of the largest and densest megacities in the world. Population mobility and fluctuations in population density are important drivers of epidemics. Measuring population mobility during outbreaks is challenging but is a particularly important goal in the context of rapidly growing and highly connected cities in low- and middle-income countries, which can act to amplify and spread local epidemics nationally and internationally.

METHODS

We first describe the epidemiology of the 2017 chikungunya outbreak in Dhaka and estimate incidence using a mechanistic model of chikungunya transmission parametrized with epidemiological data from a household survey. We combine the modeled dynamics of chikungunya in Dhaka, with mobility estimates derived from mobile phone data for over 4 million subscribers, to understand the role of population mobility on the spatial spread of chikungunya within and outside Dhaka during the 2017 outbreak.

RESULTS

We estimate a much higher incidence of chikungunya in Dhaka than suggested by official case counts. Vector abundance, local demographics, and population mobility were associated with spatial heterogeneities in incidence in Dhaka. The peak of the outbreak in Dhaka coincided with the annual Eid holidays, during which large numbers of people traveled from Dhaka to other parts of the country. We show that travel during Eid likely resulted in the spread of the infection to the rest of the country.

CONCLUSIONS

Our results highlight the impact of large-scale population movements, for example during holidays, on the spread of infectious diseases. These dynamics are difficult to capture using traditional approaches, and we compare our results to a standard diffusion model, to highlight the value of real-time data from mobile phones for outbreak analysis, forecasting, and surveillance.

Tensor decomposition for infectious disease incidence data

Many demographic and ecological processes generate seasonal and other periodicities. Seasonality in infectious disease transmission can result from climatic forces such as temperature and humidity; variation in contact rates as a result of migration or school calendar; or temporary surges in birth rates. Seasonal drivers of acute immunizing infections can also drive longer-term fluctuations.Tensor decomposition has been used in many disciplines to uncover dominant trends in multi-dimensional data. We introduce tensors as a novel method for decomposing oscillatory infectious disease time series.We illustrate the reliability of the method by applying it to simulated data. We then present decompositions of measles data from England and Wales. This paper leverages simulations as well as much-studied data to illustrate the power of tensor decomposition to uncover dominant epidemic signals as well as variation in space and time. We then use tensor decomposition to uncover new findings and demonstrate the potential power of the method for disease incidence data. In particular, we are able to distinguish between annual and biennial signals across locations and shifts in these signals over time.Tensor decomposition is able to isolate variation in disease seasonality as a result of variation in demographic rates. The method allows us to discern variation in the strength of such signals by space and population size. Tensors provide an opportunity for a concise approach to uncovering heterogeneity in disease transmission across space and time in large datasets.

Investigating persistent measles dynamics in Niger and associations with rainfall

Measles is a major cause of child mortality in sub-Saharan Africa. Current immunization strategies achieve low coverage in areas where transmission drivers differ substantially from those in high-income countries. A better understanding of measles transmission in areas with measles persistence will increase vaccination coverage and reduce ongoing transmission. We analysed weekly reported measles cases at the district level in Niger from 1995 to 2004 to identify underlying transmission mechanisms. We identified dominant periodicities and the associated spatial clustering patterns. We also investigated associations between reported measles cases and environmental drivers associated with human activities, particularly rainfall. The annual and 2-3-year periodicities dominated the reporting data spectrum. The annual periodicity was strong with contiguous spatial clustering, consistent with the latitudinal gradient of population density, and stable over time. The 2-3-year periodicities were weaker, unstable over time and had spatially fragmented clustering. The rainy season was associated with a lower risk of measles case reporting. The annual periodicity likely reflects seasonal agricultural labour migration, whereas the 2-3-year periodicity potentially results from multiple mechanisms such as reintroductions and vaccine coverage heterogeneity. Our findings suggest that improving vaccine coverage in seasonally mobile populations could reduce strong measles seasonality in Niger and across similar settings.

Structure, space and size: competing drivers of variation in urban and rural measles transmission

A key concern in public health is whether disparities exist between urban and rural areas. One dimension of potential variation is the transmission of infectious diseases. In addition to potential differences between urban and rural local dynamics, the question of whether urban and rural areas participate equally in national dynamics remains unanswered. Specifically, urban and rural areas may diverge in local transmission as well as spatial connectivity, and thus risk for receiving imported cases. Finally, the potential confounding relationship of spatial proximity with size and urban/rural district type has not been addressed by previous research. It is rare to have sufficient data to explore these questions thoroughly. We use exhaustive weekly case reports of measles in 954 urban and 468 rural districts of the UK (1944–1965) to compare both local disease dynamics as well as regional transmission. We employ the time-series susceptible–infected–recovered model to estimate disease transmission, epidemic severity, seasonality and import dependence. Congruent with past results, we observe a clear dependence on population size for the majority of these measures. We use a matched-pair strategy to compare proximate urban and rural districts and control for possible spatial confounders. This analytical strategy reveals a modest difference between urban and rural areas. Rural areas tend to be characterized by more frequent, smaller outbreaks compared to urban counterparts. The magnitude of the difference is slight and the results primarily reinforce the importance of population size, both in terms of local and regional transmission. In sum, urban and rural areas demonstrate remarkable epidemiological similarity in this recent UK context.

Spatio-temporal dynamics of measles outbreaks in Cameroon

PURPOSE

In 2012, Cameroon experienced a large measles outbreak of over 14,000 cases. To determine the spatio-temporal dynamics of measles transmission in Cameroon, we analyzed weekly case data collected by the Ministry of Health.

METHODS

We compared several multivariate time-series models of population movement to characterize the spatial spread of measles in Cameroon. Using the best model, we evaluated the contribution of population mobility to disease transmission at increasing geographic resolutions: region, department, and health district.

RESULTS

Our spatio-temporal analysis showed that the power law model, which accounts for long-distance population movement, best represents the spatial spread of measles in Cameroon. Population movement between health districts within departments contributed to 7.6% (range: 0.4%-13.4%) of cases at the district level, whereas movement between departments within regions contributed to 16.0% (range: 1.3%-23.2%) of cases. Long-distance movement between regions contributed to 16.7% (range: 0.1%-59.0%) of cases at the region level, 20.1% (range: 7.1%-30.0%) at the department level, and 29.7% (range: 15.3%-47.6%) at the health district level.

CONCLUSIONS

Population long-distance mobility is an important driver of measles dynamics in Cameroon. These findings demonstrate the need to improve our understanding of the roles of population mobility and local heterogeneity of vaccination coverage in the spread and control of measles in Cameroon.

Characteristics of measles epidemics in China (1951-2004) and implications for elimination: A case study of three key locations

Measles is a highly infectious, severe viral disease. The disease is targeted for global eradication; however, this result has proven challenging. In China, where countrywide vaccination coverage for the last decade has been above 95% (the threshold for measles elimination), measles continues to cause large epidemics. To diagnose factors contributing to the persistency of measles, here we develop a model-inference system to infer measles transmission dynamics in China. The model-inference system uses demographic and vaccination data for each year as model inputs to directly account for changing population dynamics (including births, deaths, migrations, and vaccination). In addition, it simultaneously estimates unobserved model variables and parameters based on incidence data. When fitted to yearly incidence data for the entire population, it is able to accurately estimate independent, out-of-sample age-specific incidence. Using this validated model-inference system, we are thus able to estimate epidemiological and demographical characteristics key to measles transmission during 1951-2004 for three key locations in China, including its capital Beijing. These characteristics include age-specific population susceptibility and incidence rates, the basic reproductive number (R0), reporting rate, population mixing intensity, and amplitude of seasonality. Key differences among the three sites reveal population and epidemiological characteristics crucial for understanding the current persistence of measles epidemics in China. We also discuss the implications our findings have for future elimination strategies.

Malaria intensity in Colombia by regions and populations

Determining the distribution of disease prevalence among heterogeneous populations at the national scale is fundamental for epidemiology and public health. Here, we use a combination of methods (spatial scan statistic, topological data analysis and epidemic profile) to study measurable differences in malaria intensity by regions and populations of Colombia. This study explores three main questions: What are the regions of Colombia where malaria is epidemic? What are the regions and populations in Colombia where malaria is endemic? What associations exist between epidemic outbreaks between regions in Colombia? Plasmodium falciparum is most prevalent in the Pacific Coast, some regions of the Amazon Basin, and some regions of the Magdalena Basin. Plasmodium vivax is the most prevalent parasite in Colombia, particularly in the Northern Amazon Basin, the Caribbean, and municipalities of Sucre, Antioquia and Cordoba. We find an acute peak of malarial infection at 25 years of age. Indigenous and Afrocolombian populations experience endemic malaria (with household transmission). We find that Plasmodium vivax decreased in the most important hotspots, often with moderate urbanization rate, and was re-introduced to locations with moderate but sustained deforestation. Infection by Plasmodium falciparum, on the other hand, steadily increased in incidence in locations where it was introduced in the 2009-2010 generalized epidemic. Our findings suggest that Colombia is entering an unstable transmission state, where rapid decreases in one location of the country are interconnected with rapid increases in other parts of the country.

Accounting for non-stationarity in epidemiology by embedding time-varying parameters in stochastic models

The spread of disease through human populations is complex. The characteristics of disease propagation evolve with time, as a result of a multitude of environmental and anthropic factors, this non-stationarity is a key factor in this huge complexity. In the absence of appropriate external data sources, to correctly describe the disease propagation, we explore a flexible approach, based on stochastic models for the disease dynamics, and on diffusion processes for the parameter dynamics. Using such a diffusion process has the advantage of not requiring a specific mathematical function for the parameter dynamics. Coupled with particle MCMC, this approach allows us to reconstruct the time evolution of some key parameters (average transmission rate for instance). Thus, by capturing the time-varying nature of the different mechanisms involved in disease propagation, the epidemic can be described. Firstly we demonstrate the efficiency of this methodology on a toy model, where the parameters and the observation process are known. Applied then to real datasets, our methodology is able, based solely on simple stochastic models, to reconstruct complex epidemics, such as flu or dengue, over long time periods. Hence we demonstrate that time-varying parameters can improve the accuracy of model performances, and we suggest that our methodology can be used as a first step towards a better understanding of a complex epidemic, in situation where data is limited and/or uncertain.

Contact structure, mobility, environmental impact and behaviour: the importance of social forces to infectious disease dynamics and disease ecology

Human factors, including contact structure, movement, impact on the environment and patterns of behaviour, can have significant influence on the emergence of novel infectious diseases and the transmission and amplification of established ones. As anthropogenic climate change alters natural systems and global economic forces drive land-use and land-cover change, it becomes increasingly important to understand both the ecological and social factors that impact infectious disease outcomes for human populations. While the field of disease ecology explicitly studies the ecological aspects of infectious disease transmission, the effects of the social context on zoonotic pathogen spillover and subsequent human-to-human transmission are comparatively neglected in the literature. The social sciences encompass a variety of disciplines and frameworks for understanding infectious diseases; however, here we focus on four primary areas of social systems that quantitatively and qualitatively contribute to infectious diseases as social-ecological systems. These areas are social mixing and structure, space and mobility, geography and environmental impact, and behaviour and behaviour change. Incorporation of these social factors requires empirical studies for parametrization, phenomena characterization and integrated theoretical modelling of social-ecological interactions. The social-ecological system that dictates infectious disease dynamics is a complex system rich in interacting variables with dynamically significant heterogeneous properties. Future discussions about infectious disease spillover and transmission in human populations need to address the social context that affects particular disease systems by identifying and measuring qualitatively important drivers.This article is part of the themed issue 'Opening the black box: re-examining the ecology and evolution of parasite transmission'.

Measles burden in urban settings: characteristics of measles cases in Addis Ababa city administration, Ethiopia, 2004-2014

INTRODUCTION

In developing countries, measles was a major cause of morbidity and mortality before the wide spread use of measles vaccine. The purpose of this study was to describe measles burden in an urban setting, Addis Ababa- since the implementation of measles case-based surveillance in Ethiopia. We analyzed measles surveillance data for 2004 -2014.

METHODS

Incidence of measles was described by sub city, by year and by age groups. Age specific incidence rate were calculated. Logistic regression was used to identify the predictors of confirmed measles cases.

RESULTS

Of 4220 suspected measles cases 39% were confirmed cases. Males and females were equally affected. The mean affected age was 7.59 years. Measles cases peaked in 2010 and 2013-2014. Incidence of measles is higher among children less than five years old. Outer sub cities were more affected by measles in all years.

CONCLUSION

Sub cities bordering with Oromia Regional State were more affected by measles. Older age groups were more affected than younger age groups (age ≤ five years old). Efforts to close immunity gaps against measles and further strengthen surveillance in urban settings, particularly among children below five years old, should be prioritized.

Geospatial characteristics of measles transmission in China during 2005-2014

Measles is a highly contagious and severe disease. Despite mass vaccination, it remains a leading cause of death in children in developing regions, killing 114,900 globally in 2014. In 2006, China committed to eliminating measles by 2012; to this end, the country enhanced its mandatory vaccination programs and achieved vaccination rates reported above 95% by 2008. However, in spite of these efforts, during the last 3 years (2013-2015) China documented 27,695, 52,656, and 42,874 confirmed measles cases. How measles manages to spread in China-the world's largest population-in the mass vaccination era remains poorly understood. To address this conundrum and provide insights for future public health efforts, we analyze the geospatial pattern of measles transmission across China during 2005-2014. We map measles incidence and incidence rates for each of the 344 cities in mainland China, identify the key socioeconomic and demographic features associated with high disease burden, and identify transmission clusters based on the synchrony of outbreak cycles. Using hierarchical cluster analysis, we identify 21 epidemic clusters, of which 12 were cross-regional. The cross-regional clusters included more underdeveloped cities with large numbers of emigrants than would be expected by chance (p = 0.011; bootstrap sampling), indicating that cities in these clusters were likely linked by internal worker migration in response to uneven economic development. In contrast, cities in regional clusters were more likely to have high rates of minorities and high natural growth rates than would be expected by chance (p = 0.074; bootstrap sampling). Our findings suggest that multiple highly connected foci of measles transmission coexist in China and that migrant workers likely facilitate the transmission of measles across regions. This complex connection renders eradication of measles challenging in China despite its high overall vaccination coverage. Future immunization programs should therefore target these transmission foci simultaneously.

Community unit performance: factors associated with childhood diarrhea and appropriate treatment in Nyanza Province, Kenya

Background

The government of Kenya launched its community health strategy in 2006 to improve certain aspects of its community health program. Under the strategy, community units (CUs) were established as level one of the Kenyan health system. A core member at this level is the community health worker (CHW). The objective of this study was to assess the relationship among the performance of the CUs, the prevalence of childhood diarrhea and appropriate treatment for it by controlling individual and community-level factors.

Methods

The main dataset used in this study was the 2011 Nyanza Province county-based Multiple Indicator Cluster Survey (MICS). In addition, based on the list of community units in Nyanza Province, Kenya, we identified the area’s CUs and their performance. MICS data and data on CUs were merged using sub-location names. There were 17 individual and two community-level independent variables in this study. Bivariate analysis and a multilevel logistic regression were performed.

Results

Factors significantly associated with a lower prevalence of diarrhea among children under five were the child’s increasing age, middle-aged household heads, children who received more attention, water treatment and rural versus urban area residence, while male children and highly performing CUs were significantly associated with a higher prevalence of diarrhea. In addition, middle wealth index, severity of diarrhea and middle- and high-CU performance were significantly associated with appropriate treatment for childhood diarrhea.

Conclusions

Although this study found that children living in areas of high CU performance were more likely to have diarrhea, these areas would have been identified as being more at risk for diarrhea prevalence and other health concerns, prioritized for the establishment of a CU and allocated more resources to improve the performance of CUs. A higher CU performance was significantly associated with the appropriate treatment. It was suggested that CHWs could have a positive effect on the community, as demonstrated and promoted by appropriate health-seeking behavior and treatment for childhood diarrhea.

Comparative dynamics, seasonality in transmission, and predictability of childhood infections in Mexico

The seasonality and periodicity of infections, and the mechanisms underlying observed dynamics, can have implications for control efforts. This is particularly true for acute childhood infections. Among these, the dynamics of measles is the best understood and has been extensively studied, most notably in the UK prior to the start of vaccination. Less is known about the dynamics of other childhood diseases, particularly outside Europe and the United States. In this paper, we leverage a unique dataset to examine the epidemiology of six childhood infections - measles, mumps, rubella, varicella, scarlet fever and pertussis - across 32 states in Mexico from 1985 to 2007. This dataset provides us with a spatio-temporal probe into the dynamics of six common childhood infections, and allows us to compare them in the same setting over the same time period. We examine three key epidemiological characteristics of these infections - the age profile of infections, spatio-temporal dynamics, and seasonality in transmission - and compare them with predictions from existing theory and past findings. Our analysis reveals interesting epidemiological differences between the six pathogens, and variations across space. We find signatures of term-time forcing (reduced transmission during the summer) for measles, mumps, rubella, varicella, and scarlet fever; for pertussis, a lack of term-time forcing could not be rejected.

Measuring populations to improve vaccination coverage

In low-income settings, vaccination campaigns supplement routine immunization but often fail to achieve coverage goals due to uncertainty about target population size and distribution. Accurate, updated estimates of target populations are rare but critical; short-term fluctuations can greatly impact population size and susceptibility. We use satellite imagery to quantify population fluctuations and the coverage achieved by a measles outbreak response vaccination campaign in urban Niger and compare campaign estimates to measurements from a post-campaign survey. Vaccine coverage was overestimated because the campaign underestimated resident numbers and seasonal migration further increased the target population. We combine satellite-derived measurements of fluctuations in population distribution with high-resolution measles case reports to develop a dynamic model that illustrates the potential improvement in vaccination campaign coverage if planners account for predictable population fluctuations. Satellite imagery can improve retrospective estimates of vaccination campaign impact and future campaign planning by synchronizing interventions with predictable population fluxes.

A spatial model of Wild Poliovirus Type 1 in Kano State, Nigeria: calibration and assessment of elimination probability

BACKGROUND

Since the launch of the Global Polio Eradication Initiative, all but three countries (Nigeria, Pakistan, and Afghanistan) have apparently interrupted all wild poliovirus (WPV) transmission, and only one of three wild serotypes has been reported globally since 2012. Countrywide supplemental immunization campaigns in Nigeria produced dramatic reduction in WPV Type 1 paralysis cases since 2010 compared to the 2000's, and WPV1 has not been observed in Nigeria since July 24, 2014. This article presents the development and calibration of a spatial metapopulation model of wild poliovirus Type 1 transmission in Kano State, Nigeria, which was the location of the most recent WPV1 case and 5 out of 6 of the reported WPV1 paralytic cases in Nigeria in 2014.

METHODS

The model is calibrated to data on the case counts and age at onset of paralysis from 2003-2009. The features of the data drive model development from a simple susceptible-exposed-infective-recovered (SEIR) model to a spatial metapopulation model featuring seasonal forcing and age-dependent transmission. The calibrated parameter space is then resampled, projected forward, and compared to more recent case counts to estimate the probability that Type 1 poliovirus has been eliminated in Kano state.

RESULTS

The model indicates a 91 % probability that Type 1 poliovirus has been eliminated from Kano state as of October 2015. This probability rises to >99 % if no WPV1 paralysis cases are detected for another year. The other states in Nigeria have experienced even longer case-free periods (the only other state with a WPV1 case was Yobe, on April 19, 2014), and Nigeria is the last remaining country in Africa to experience endemic WPV1 transmission, so these results can be interpreted as an upper bound on the probability that WPV1 transmission is currently interrupted continent-wide.

CONCLUSIONS

While the results indicate optimism that WPV1 transmission has been interrupted in Kano state, the model also assumes that frequent SIAs with high coverage continue to take place in Kano state through the end of the certification period. We conclude that though WPV1 appears to be on the brink of continent-wide elimination (WHO officially removed Nigeria from the list of polio-endemic countries on September 25, 2015), it is important for the polio program to maintain vigilance in surveillance and vaccination activities to prevent WPV1 resurgence through the WHO's 3-year eradication certification period.

Hand, Foot, and Mouth Disease in China: Critical Community Size and Spatial Vaccination Strategies

Hand Foot and Mouth Disease (HFMD) constitutes a considerable burden for health care systems across China. Yet this burden displays important geographic heterogeneity that directly affects the local persistence and the dynamics of the disease, and thus the ability to control it through vaccination campaigns. Here, we use detailed geographic surveillance data and epidemic models to estimate the critical community size (CCS) of HFMD associated enterovirus serotypes CV-A16 and EV-A71 and we explore what spatial vaccination strategies may best reduce the burden of HFMD. We found CCS ranging from 336,979 (±225,866) to 722,372 (±150,562) with the lowest estimates associated with EV-A71 in the southern region of China where multiple transmission seasons have previously been identified. Our results suggest the existence of a regional immigration-recolonization dynamic driven by urban centers. If EV-A71 vaccines doses are limited, these would be optimally deployed in highly populated urban centers and in high-prevalence areas. If HFMD vaccines are included in China's National Immunization Program in order to achieve high coverage rates (>85%), routine vaccination of newborns largely outperforms strategies in which the equivalent number of doses is equally divided between routine vaccination of newborns and pulse vaccination of the community at large.

Seasonality and the effectiveness of mass vaccination

Many infectious diseases have seasonal outbreaks, which may be driven by cyclical environmental conditions (e.g., an annual rainy season) or human behavior (e.g., school calendars or seasonal migration). If a pathogen is only transmissible for a limited period of time each year, then seasonal outbreaks could infect fewer individuals than expected given the pathogen's in-season transmissibility. Influenza, with its short serial interval and long season, probably spreads throughout a population until a substantial fraction of susceptible individuals are infected. Dengue, with a long serial interval and shorter season, may be constrained by its short transmission season rather than the depletion of susceptibles. Using mathematical modeling, we show that mass vaccination is most efficient, in terms of infections prevented per vaccine administered, at high levels of coverage for pathogens that have relatively long epidemic seasons, like influenza, and at low levels of coverage for pathogens with short epidemic seasons, like dengue. Therefore, the length of a pathogen's epidemic season may need to be considered when evaluating the costs and benefits of vaccination programs.

Spatial clustering of measles cases during endemic (1998-2002) and epidemic (2010) periods in Lusaka, Zambia

BACKGROUND

Measles cases may cluster in densely populated urban centers in sub-Saharan Africa as susceptible individuals share spatially dependent risk factors and may cluster among human immunodeficiency virus (HIV)-infected children despite high vaccination coverage.

METHODS

Children hospitalized with measles at the University Teaching Hospital (UTH) in Lusaka, Zambia were enrolled in the study. The township of residence was recorded on the questionnaire and mapped; SaTScan software was used for cluster detection. A spatial-temporal scan statistic was used to investigate clustering of measles in children hospitalized during an endemic period (1998 to 2002) and during the 2010 measles outbreak in Lusaka, Zambia.

RESULTS

Three sequential and spatially contiguous clusters of measles cases were identified during the 2010 outbreak but no clustering among HIV-infected children was identified. In contrast, a space-time cluster among HIV-infected children was identified during the endemic period. This cluster occurred prior to the introduction of intensive measles control efforts and during a period between seasonal peaks in measles incidence.

CONCLUSIONS

Prediction and early identification of spatial clusters of measles will be critical to achieving measles elimination. HIV infection may contribute to spatial clustering of measles cases in some epidemiological settings.

On the use of human mobility proxies for modeling epidemics

Human mobility is a key component of large-scale spatial-transmission models of infectious diseases. Correctly modeling and quantifying human mobility is critical for improving epidemic control, but may be hindered by data incompleteness or unavailability. Here we explore the opportunity of using proxies for individual mobility to describe commuting flows and predict the diffusion of an influenza-like-illness epidemic. We consider three European countries and the corresponding commuting networks at different resolution scales, obtained from (i) official census surveys, (ii) proxy mobility data extracted from mobile phone call records, and (iii) the radiation model calibrated with census data. Metapopulation models defined on these countries and integrating the different mobility layers are compared in terms of epidemic observables. We show that commuting networks from mobile phone data capture the empirical commuting patterns well, accounting for more than 87% of the total fluxes. The distributions of commuting fluxes per link from mobile phones and census sources are similar and highly correlated, however a systematic overestimation of commuting traffic in the mobile phone data is observed. This leads to epidemics that spread faster than on census commuting networks, once the mobile phone commuting network is considered in the epidemic model, however preserving to a high degree the order of infection of newly affected locations. Proxies' calibration affects the arrival times' agreement across different models, and the observed topological and traffic discrepancies among mobility sources alter the resulting epidemic invasion patterns. Results also suggest that proxies perform differently in approximating commuting patterns for disease spread at different resolution scales, with the radiation model showing higher accuracy than mobile phone data when the seed is central in the network, the opposite being observed for peripheral locations. Proxies should therefore be chosen in light of the desired accuracy for the epidemic situation under study.

The effects of school holidays on transmission of varicella zoster virus, England and Wales, 1967-2008

Background

Changes in children’s contact patterns between termtime and school holidays affect the transmission of several respiratory-spread infections. Transmission of varicella zoster virus (VZV), the causative agent of chickenpox, has also been linked to the school calendar in several settings, but temporal changes in the proportion of young children attending childcare centres may have influenced this relationship.

Methods

We used two modelling methods (a simple difference equations model and a Time series Susceptible Infectious Recovered (TSIR) model) to estimate fortnightly values of a contact parameter (the per capita rate of effective contact between two specific individuals), using GP consultation data for chickenpox in England and Wales from 1967–2008.

Results

The estimated contact parameters were 22–31% lower during the summer holiday than during termtime. The relationship between the contact parameter and the school calendar did not change markedly over the years analysed.

Conclusions

In England and Wales, reductions in contact between children during the school summer holiday lead to a reduction in the transmission of VZV. These estimates are relevant for predicting how closing schools and nurseries may affect an outbreak of an emerging respiratory-spread pathogen.

Metapopulation dynamics enable persistence of influenza A, including A/H5N1, in poultry

Highly pathogenic influenza A/H5N1 has persistently but sporadically caused human illness and death since 1997. Yet it is still unclear how this pathogen is able to persist globally. While wild birds seem to be a genetic reservoir for influenza A, they do not seem to be the main source of human illness. Here, we highlight the role that domestic poultry may play in maintaining A/H5N1 globally, using theoretical models of spatial population structure in poultry populations. We find that a metapopulation of moderately sized poultry flocks can sustain the pathogen in a finite poultry population for over two years. Our results suggest that it is possible that moderately intensive backyard farms could sustain the pathogen indefinitely in real systems. This fits a pattern that has been observed from many empirical systems. Rather than just employing standard culling procedures to control the disease, our model suggests ways that poultry production systems may be modified.

Characterizing the dynamics of rubella relative to measles: the role of stochasticity

Rubella is a completely immunizing and mild infection in children. Understanding its behaviour is of considerable public health importance because of congenital rubella syndrome, which results from infection with rubella during early pregnancy and may entail a variety of birth defects. The recurrent dynamics of rubella are relatively poorly resolved, and appear to show considerable diversity globally. Here, we investigate the behaviour of a stochastic seasonally forced susceptible-infected-recovered model to characterize the determinants of these dynamics and illustrate patterns by comparison with measles. We perform a systematic analysis of spectra of stochastic fluctuations around stable attractors of the corresponding deterministic model and compare them with spectra from full stochastic simulations in large populations. This approach allows us to quantify the effects of demographic stochasticity and to give a coherent picture of measles and rubella dynamics, explaining essential differences in the recurrent patterns exhibited by these diseases. We discuss the implications of our findings in the context of vaccination and changing birth rates as well as the persistence of these two childhood infections.

Impact of birth seasonality on dynamics of acute immunizing infections in Sub-Saharan Africa

We analyze the impact of birth seasonality (seasonal oscillations in the birth rate) on the dynamics of acute, immunizing childhood infectious diseases. Previous research has explored the effect of human birth seasonality on infectious disease dynamics using parameters appropriate for the developed world. We build on this work by including in our analysis an extended range of baseline birth rates and amplitudes, which correspond to developing world settings. Additionally, our analysis accounts for seasonal forcing both in births and contact rates. We focus in particular on the dynamics of measles. In the absence of seasonal transmission rates or stochastic forcing, for typical measles epidemiological parameters, birth seasonality induces either annual or biennial epidemics. Changes in the magnitude of the birth fluctuations (birth amplitude) can induce significant changes in the size of the epidemic peaks, but have little impact on timing of disease epidemics within the year. In contrast, changes to the birth seasonality phase (location of the peak in birth amplitude within the year) significantly influence the timing of the epidemics. In the presence of seasonality in contact rates, at relatively low birth rates (20 per 1000), birth amplitude has little impact on the dynamics but does have an impact on the magnitude and timing of the epidemics. However, as the mean birth rate increases, both birth amplitude and phase play an important role in driving the dynamics of the epidemic. There are stronger effects at higher birth rates.

The impact of declining vaccination coverage on measles control: a case study of Abia state Nigeria

Introduction

Efforts at immunizing children against measles was intensified in Nigeria with nation-wide measles vaccination campaigns in 2005 - 2006, 2008 and 2011 targeting children between 9 and 59 months. However, there were measles outbreaks in 2010 and 2011in Abia state Nigeria. This study seeks to find out if there is any association between measles immunization coverage and measles outbreak.

Methods

This is a descriptive analysis of the 2007 to 2011 Abia state measles case-based surveillance data supplied to Abia state World Health Organization office and Abia State Ministry of Health by the disease surveillance and notification officers.

Results

As the proportion of cases with febrile rash who were immunized decreased from 81% in 2007 to 42% in 2011, the laboratory confirmed cases of measles increased from two in 2007 to 53 in 2011.Of the laboratory confirmed cases of measles, five (7%) occurred in children < 9 months, 48 (64%) occurred in children 9 - 59 months and 22 (29%) occurred in children < 59 months old. Seventy five percent of all laboratory confirmed cases of measles occurred in rural areas.

Conclusion

Efforts should be made to increase measles immunization in children between 9 and 59 months as most cases of measles occurred in this age group as immunization coverage dropped. In addition, further studies should be carried out to determine the cause of the disproportional incidence of measles in rural areas in Abia state bearing in mind that measles immunization coverage in urban and rural areas was not markedly different

Implications of spatially heterogeneous vaccination coverage for the risk of congenital rubella syndrome in South Africa

Rubella is generally a mild childhood disease, but infection during early pregnancy may cause spontaneous abortion or congenital rubella syndrome (CRS), which may entail a variety of birth defects. Since vaccination at levels short of those necessary to achieve eradication may increase the average age of infection, and thus potentially the CRS burden, introduction of the vaccine has been limited to contexts where coverage is high. Recent work suggests that spatial heterogeneity in coverage should also be a focus of concern. Here, we use a detailed dataset from South Africa to explore the implications of heterogeneous vaccination for the burden of CRS, introducing realistic vaccination scenarios based on reported levels of measles vaccine coverage. Our results highlight the potential impact of country-wide reductions of incidence of rubella on the local CRS burdens in districts with small population sizes. However, simulations indicate that if rubella vaccination is introduced with coverage reflecting current estimates for measles coverage in South Africa, the burden of CRS is likely to be reduced overall over a 30 year time horizon by a factor of 3, despite the fact that this coverage is lower than the traditional 80 per cent rule of thumb for vaccine introduction, probably owing to a combination of relatively low birth and transmission rates. We conclude by discussing the likely impact of private-sector vaccination.

Effect of thermal environment on the temporal, spatial and seasonal occurrence of measles in Ondo state, Nigeria

We investigated the temporal and spatial dynamics, as well as the seasonal occurrence of measles in Ondo state, Nigeria, to better understand the role of the thermal environment in the occurrence of the childhood killer disease measles, which ranks among the top ten leading causes of child deaths worldwide. The linkages between measles and atmospheric environmental factors were examined by correlating human-biometeorological parameters in the study area with reported clinical cases of measles for the period 1998-2008. We also applied stepwise regression analysis in order to determine the human-biometeorological parameters that lead to statistical changes in reported clinical cases of measles. We found that high reported cases of measles are associated with the least populated areas, where rearing and cohabitation of livestock/domestic animals within human communities are common. There was a significant correlation (P < 0.01) between monthly cases of measles and human-biometeorological parameters except wind speed and vapour pressure. High transmission of measles occurred in the months of January to May during the dry season when human thermal comfort indices are very high. This highlights the importance of the thermal environment in disease demographics since it accounted for more than 40% variation in measles transmission within the study period.

Prediction of child health by household density and asset-based indices in impoverished indigenous villages in rural Panamá

Chronic infection over a 16-month period and stunting of preschool children were compared between more spatially dense versus dispersed households in rural Panamá. Chronic protozoan infection was associated with higher household density, lower household wealth index, poor household water quality, yard defecation, and the practice of not washing hands with soap before eating. Models for chronic diarrhea confirmed the importance of household wealth, water quality, sanitation, and hygiene practices. Furthermore, chronic protozoan infection was an important predictor for low height-for-age, along with low household wealth index scores, but not household density. Thus, despite better access to health related infrastructure in the more densely populated households, chronic protozoan infection was more common, and was associated with higher rates of child stunting, compared with more dispersed households.

Explaining seasonal fluctuations of measles in Niger using nighttime lights imagery

Measles epidemics in West Africa cause a significant proportion of vaccine-preventable childhood mortality. Epidemics are strongly seasonal, but the drivers of these fluctuations are poorly understood, which limits the predictability of outbreaks and the dynamic response to immunization. We show that measles seasonality can be explained by spatiotemporal changes in population density, which we measure by quantifying anthropogenic light from satellite imagery. We find that measles transmission and population density are highly correlated for three cities in Niger. With dynamic epidemic models, we demonstrate that measures of population density are essential for predicting epidemic progression at the city level and improving intervention strategies. In addition to epidemiological applications, the ability to measure fine-scale changes in population density has implications for public health, crisis management, and economic development.

Temporal variation in temperature determines disease spread and maintenance in Paramecium microcosm populations

The environment is rarely constant and organisms are exposed to temporal and spatial variations that impact their life histories and inter-species interactions. It is important to understand how such variations affect epidemiological dynamics in host-parasite systems. We explored effects of temporal variation in temperature on experimental microcosm populations of the ciliate Paramecium caudatum and its bacterial parasite Holospora undulata. Infected and uninfected populations of two P. caudatum genotypes were created and four constant temperature treatments (26°C, 28°C, 30°C and 32°C) compared with four variable treatments with the same mean temperatures. Variable temperature treatments were achieved by alternating populations between permissive (23°C) and restrictive (35°C) conditions daily over 30 days. Variable conditions and high temperatures caused greater declines in Paramecium populations, greater fluctuations in population size and higher incidence of extinction. The additional effect of parasite infection was additive and enhanced the negative effects of the variable environment and higher temperatures by up to 50 per cent. The variable environment and high temperatures also caused a decrease in parasite prevalence (up to 40%) and an increase in extinction (absence of detection) (up to 30%). The host genotypes responded similarly to the different environmental stresses and their effect on parasite traits were generally in the same direction. This work provides, to our knowledge, the first experimental demonstration that epidemiological dynamics are influenced by environmental variation. We also emphasize the need to consider environmental variance, as well as means, when trying to understand, or predict population dynamics or range.