Impact of disruptions to routine vaccination programs, quantifying burden of measles, and mapping targeted supplementary immunization activities.

Measles is a highly transmissible disease that requires high levels of vaccination coverage for control and elimination. Areas that are unable to achieve and maintain high coverage levels are at risk for measles outbreaks resulting in increased morbidity and mortality. Public health emergencies, such as the current COVID-19 pandemic, pose a threat to the functioning of health systems by disrupting immunization services which can derail measles vaccination efforts. Efforts to bridge coverage gaps in immunization include the rapid return to fully functioning services as well as deploying supplementary immunization activities (SIAs), which are additional vaccination campaigns intended to catch-up children who have missed routine services. However, SIAs, which to date tend to be national efforts, can be difficult to mobilize quickly, resource-intensive, and even more challenging to deploy during a public health crisis. By mapping expected burden of measles, more effective SIAs that are setting-specific and resource-efficient can be planned and mobilized. Using a spatial transmission model of measles dynamics, we projected and estimated the expected burden of national and local measles outbreaks in Zambia with the current COVID-19 pandemic as a framework to inform disruptions to routine vaccination. We characterize the impact of disruptions to routine immunization services on measles incidence, map expected case burden, and explore SIA strategies to mitigate measles outbreaks. We find that disruptions lasting six months or longer as well as having low MCV1 coverage prior to disruptions resulted in an observable increase of measles cases across provinces. Targeting provinces at higher risk of measles outbreaks for SIAs is an effective strategy to curb measles virus incidence following disruptions to routine immunization services.

Leveraging serology to titrate immunisation programme functionality for diphtheria in Madagascar.

Diphtheria is a potentially devastating disease whose epidemiology remains poorly described in many settings, including Madagascar. Diphtheria vaccination is delivered in combination with pertussis and tetanus antigens and coverage of this vaccine is often used as a core measure of health system functioning. However, coverage is challenging to estimate due to the difficulty in translating numbers of doses delivered into numbers of children effectively immunised. Serology provides an alternative lens onto immunisation, but is complicated by challenges in discriminating between natural and vaccine-derived seropositivity. Here, we leverage known features of the serological profile of diphtheria to bound expectations for vaccine coverage for diphtheria, and further refine these using serology for pertussis. We measured diphtheria antibody titres in 185 children aged 6-11 months and 362 children aged 8-15 years and analysed them with pertussis antibody titres previously measured for each individual. Levels of diphtheria seronegativity varied among age groups (18.9% of children aged 6-11 months old and 11.3% of children aged 8-15 years old were seronegative) and also among the districts. We also find surprisingly elevated levels of individuals seropositive to diphtheria but not pertussis in the 6-11 month old age group suggesting that vaccination coverage or efficacy of the pertussis component of the DTP vaccine remains low or that natural infection of diphtheria may be playing a significant role in seropositivity in Madagascar.

Differential drivers of intraspecific and interspecific competition during malaria-helminth co-infection.

Various host and parasite factors interact to determine the outcome of infection. We investigated the effects of two factors on the within-host dynamics of malaria in mice: initial infectious dose and co-infection with a helminth that limits the availability of red blood cells (RBCs). Using a statistical, time-series approach to model the within-host 'epidemiology' of malaria, we found that increasing initial dose reduced the time to peak cell-to-cell parasite propagation, but also reduced its magnitude, while helminth co-infection delayed peak cell-to-cell propagation, except at the highest malaria doses. Using a mechanistic model of within-host infection dynamics, we identified dose-dependence in parameters describing host responses to malaria infection and uncovered a plausible explanation of the observed differences in single vs co-infections. Specifically, in co-infections, our model predicted a higher background death rate of RBCs. However, at the highest dose, when intraspecific competition between malaria parasites would be highest, these effects of co-infection were not observed. Such interactions between initial dose and co-infection, although difficult to predict a priori, are key to understanding variation in the severity of disease experienced by hosts and could inform studies of malaria transmission dynamics in nature, where co-infection and low doses are the norm.

Seroprevalence of pertussis in Madagascar and implications for vaccination.

Pertussis is a highly contagious infectious disease and remains an important cause of mortality and morbidity worldwide. Over the last decade, vaccination has greatly reduced the burden of pertussis. Yet, uncertainty in individual vaccination coverage and ineffective case surveillance systems make it difficult to estimate burden and the related quantity of population-level susceptibility, which determines population risk. These issues are more pronounced in low-income settings where coverage is often overestimated, and case numbers are under-reported. Serological data provide a direct characterisation of the landscape of susceptibility to infection; and can be combined with vaccination coverage and basic theory to estimate rates of exposure to natural infection. Here, we analysed cross-sectional data on seropositivity against pertussis to identify spatial and age patterns of susceptibility in children in Madagascar. A large proportion of individuals surveyed were seronegative; however, there were patterns suggestive of natural infection in all the regions analysed. Improvements in vaccination coverage are needed to help prevent additional burden of pertussis in the country.

The duration of travel impacts the spatial dynamics of infectious diseases.

Humans can impact the spatial transmission dynamics of infectious diseases by introducing pathogens into susceptible environments. The rate at which this occurs depends in part on human-mobility patterns. Increasingly, mobile-phone usage data are used to quantify human mobility and investigate the impact on disease dynamics. Although the number of trips between locations and the duration of those trips could both affect infectious-disease dynamics, there has been limited work to quantify and model the duration of travel in the context of disease transmission. Using mobility data inferred from mobile-phone calling records in Namibia, we calculated both the number of trips between districts and the duration of these trips from 2010 to 2014. We fit hierarchical Bayesian models to these data to describe both the mean trip number and duration. Results indicate that trip duration is positively related to trip distance, but negatively related to the destination population density. The highest volume of trips and shortest trip durations were among high-density districts, whereas trips among low-density districts had lower volume with longer duration. We also analyzed the impact of including trip duration in spatial-transmission models for a range of pathogens and introduction locations. We found that inclusion of trip duration generally delays the rate of introduction, regardless of pathogen, and that the variance and uncertainty around spatial spread increases proportionally with pathogen-generation time. These results enhance our understanding of disease-dispersal dynamics driven by human mobility, which has potential to elucidate optimal spatial and temporal scales for epidemic interventions.

Using Serology to Anticipate Measles Post-honeymoon Period Outbreaks.

Measles vaccination is a public health 'best buy', with the highest cost of illness averted of any vaccine-preventable disease (Ozawa et al., Bull. WHO 2017;95:629). In recent decades, substantial reductions have been made in the number of measles cases, with an estimated 20 million deaths averted from 2000 to 2017 (Dabbagh et al., MMWR 2018;67:1323). Yet, an important feature of epidemic dynamics is that large outbreaks can occur following years of apparently successful control (Mclean et al., Epidemiol. Infect. 1988;100:419-442). Such 'post-honeymoon period' outbreaks are a result of the nonlinear dynamics of epidemics (Mclean et al., Epidemiol. Infect. 1988;100:419-442). Anticipating post-honeymoon outbreaks could lead to substantial gains in public health, helping to guide the timing, age-range, and location of catch-up vaccination campaigns (Grais et al., J. Roy. Soc. Interface 2008003B6:67-74). Theoretical conditions for such outbreaks are well understood for measles, yet the information required to make these calculations policy-relevant is largely lacking. We propose that a major extension of serological studies to directly characterize measles susceptibility is a high priority.

Using models to shape measles control and elimination strategies in low- and middle-income countries: A review of recent applications.

After many decades of vaccination, measles epidemiology varies greatly between and within countries. National immunization programs are therefore encouraged to conduct regular situation analyses and to leverage models to adapt interventions to local needs. Here, we review applications of models to develop locally tailored interventions to support control and elimination efforts. In general, statistical and semi-mechanistic transmission models can be used to synthesize information from vaccination coverage, measles incidence, demographic, and/or serological data, offering a means to estimate the spatial and age-specific distribution of measles susceptibility. These estimates complete the picture provided by vaccination coverage alone, by accounting for natural immunity. Dynamic transmission models can then be used to evaluate the relative impact of candidate interventions for measles control and elimination and the expected future epidemiology. In most countries, models predict substantial numbers of susceptible individuals outside the age range of routine vaccination, which affects outbreak risk and necessitates additional intervention to achieve elimination. More effective use of models to inform both vaccination program planning and evaluation requires the development of training to enhance broader understanding of models and where feasible, building capacity for modelling in-country, pipelines for rapid evaluation of model predictions using surveillance data, and clear protocols for incorporating model results into decision-making.

Seasonal gaps in measles vaccination coverage in Madagascar.

INTRODUCTION: Measles elimination depends on the successful deployment of measles containing vaccine. Vaccination programs often depend on a combination of routine and non-routine services, including supplementary immunization activities (SIAs) and vaccination weeks (VWs), that both aim to vaccinate all eligible children regardless of vaccination history or natural infection. Madagascar has used a combination of these activities to improve measles coverage. However, ongoing massive measles outbreak suggests that the country was in a "honeymoon" period and that coverage achieved needs to be re-evaluated. Although healthcare access is expected to vary seasonally in low resources settings, little evidence exists to quantify temporal fluctuations in routine vaccination, and interactions with other immunization activities. METHODS: We used three data sources: national administrative data on measles vaccine delivery from 2013 to 2016, digitized vaccination cards from 49 health centers in 6 health districts, and a survey of health workers. Data were analyzed using linear regressions, analysis of variance, and t-tests. FINDINGS: From 2013 to 2016, the footprint of SIAs and VWs is apparent, with more doses distributed during the relevant timeframes. Routine vaccination decreases in subsequent months, suggesting that additional activities may be interfering with routine services. The majority of missed vaccination opportunities occur during the rainy season. Health facility organization and shortage of vaccine contributed to vaccination gaps. Children born in June were the least likely to be vaccinated on time. DISCUSSION: Evidence that routine vaccination coverage varies over the year and is diminished by other activities suggests that maintaining routine vaccination during SIAs and VWs is a key direction for strengthening immunization programs, ensuring population immunity and avoiding future outbreaks. FUNDING: Wellcome Trust Fund, Burroughs Wellcome Fund, Gates Foundation, National Institutes of Health.

Estimating sources and sinks of malaria parasites in Madagascar.

In areas where malaria epidemiology is spatially and temporally heterogeneous, human-mediated parasite importation can result in non-locally acquired clinical cases and outbreaks in low-transmission areas. Using mobility estimates derived from the mobile phone data and spatial malaria prevalence data, we identify travel routes relevant to malaria transmission in Madagascar. We find that the primary hubs of parasite importation are in a spatially connected area of the central highlands. Surprisingly, sources of these imported infections are not spatially clustered. We then related these source locations directly to clinical cases in the low-transmission area of the capital. We find that in the capital, a major sink, the primary sources of infection are along the more populated coastal areas, although these sources are seasonally variable. Our results have implications for targeting interventions at source locations to achieve local or national malaria control goals.

Rubella vaccination in India: identifying broad consequences of vaccine introduction and key knowledge gaps.

Rubella virus infection typically presents as a mild illness in children; however, infection during pregnancy may cause the birth of an infant with congenital rubella syndrome (CRS). As of February 2017, India began introducing rubella-containing vaccine (RCV) into the public-sector childhood vaccination programme. Low-level RCV coverage among children over several years can result in an increase in CRS incidence by increasing the average age of infection without sufficiently reducing rubella incidence. We evaluated the impact of RCV introduction on CRS incidence across India's heterogeneous demographic and epidemiological contexts. We used a deterministic age-structured model that reflects Indian states' rural and urban area-specific demography and vaccination coverage levels to simulate rubella dynamics and estimate CRS incidence with and without RCV introduction to the public sector. Our analysis suggests that current low-level private-sector vaccination has already slightly increased the burden of CRS in India. We additionally found that the effect of public-sector RCV introduction depends on the basic reproductive number, R 0, of rubella. If R 0 is five, a value empirically estimated from an array of settings, CRS incidence post-RCV introduction will likely decrease. However, if R 0 is seven or nine, some states may experience short-term or annual increases in CRS, even if a long-term total reduction in cases (30 years) is expected. Investment in population-based serological surveys and India's fever/rash surveillance system will be key to monitoring the success of the vaccination programme.

Multinational patterns of seasonal asymmetry in human movement influence infectious disease dynamics.

Seasonal variation in human mobility is globally ubiquitous and affects the spatial spread of infectious diseases, but the ability to measure seasonality in human movement has been limited by data availability. Here, we use mobile phone data to quantify seasonal travel and directional asymmetries in Kenya, Namibia, and Pakistan, across a spectrum from rural nomadic populations to highly urbanized communities. We then model how the geographic spread of several acute pathogens with varying life histories could depend on country-wide connectivity fluctuations through the year. In all three countries, major national holidays are associated with shifts in the scope of travel. Within this broader pattern, the relative importance of particular routes also fluctuates over the course of the year, with increased travel from rural to urban communities after national holidays, for example. These changes in travel impact how fast communities are likely to be reached by an introduced pathogen.

Drivers of measles mortality: the historic fatality burden of famine in Bangladesh.

Measles is a major cause of childhood morbidity and mortality in many parts of the world. Estimates of the case-fatality rate (CFR) of measles have varied widely from place to place, as well as in the same location over time. Amongst populations that have experienced famine or armed conflict, measles CFR can be especially high, although past work has mostly focused on refugee populations. Here, we estimate measles CFR between 1970 and 1991 in a rural region of Bangladesh, which experienced civil war and famine in the 1970s. We use historical measles mortality data and a mechanistic model of measles transmission to estimate the CFR of measles. We first demonstrate the ability of this model to recover the CFR in the absence of incidence data, using simulated mortality data. Our method produces CFR estimates that correspond closely to independent estimates from surveillance data and we can capture both the magnitude and the change in CFR suggested by these previous estimates. We use this method to quantify the sharp increase in CFR that resulted in a large number of deaths during a measles outbreak in the region in 1976. Most of the children who died during this outbreak were born during a famine in 1974, or in the 2 years preceding the famine. Our results suggest that the period of turmoil during and after the 1971 war and the sustained effects of the famine, is likely to have contributed to the high fatality burden of the 1976 measles outbreak in Matlab.

Demographics, epidemiology and the impact of vaccination campaigns in a measles-free world - Can elimination be maintained?

INTRODUCTION: All six WHO regions currently have goals for measles elimination by 2020. Measles vaccination is delivered via routine immunization programmes, which in most sub-Saharan African countries reach children around 9months of age, and supplementary immunization activities (SIAs), which target a wider age range at multi-annual intervals. In the absence of endemic measles circulation, the proportion of individuals susceptible to measles will gradually increase through accumulation of new unvaccinated individuals in each birth cohort, increasing the risk of an epidemic. The impact of SIAs and the financial investment they require, depend on coverage and target age range. MATERIALS AND METHODS: We evaluated the impact of target population age range for periodic SIAs, evaluating outcomes for two different levels of coverage, using a demographic and epidemiological model adapted to reflect populations in 4 sub-Saharan African countries. RESULTS: We found that a single SIA can maintain elimination over short time-scales, even with low routine coverage. However, maintaining elimination for more than a few years is difficult, even with large (high coverage/wide age range) recurrent SIAs, due to the build-up of susceptible individuals. Across the demographic and vaccination contexts investigated, expanding SIAs to target individuals over 10years did not significantly reduce outbreak risk. CONCLUSIONS: Elimination was not maintained in the contexts we evaluated without a second opportunity for vaccination. In the absence of an expanded routine program, SIAs provide a powerful option for providing this second dose. We show that a single high coverage SIA can deliver most key benefits in terms of maintaining elimination, with follow-up campaigns potentially requiring smaller investments. This makes post-campaign evaluation of coverage increasingly relevant to correctly assess future outbreak risk.

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.

The effects of host age and spatial location on bacterial community composition in the English Oak tree (Quercus robur).

Drivers of bacterial community assemblages associated with plants are diverse and include biotic factors, such as competitors and host traits, and abiotic factors, including environmental conditions and dispersal mechanisms. We examine the roles of spatial distribution and host size, as an approximation for age, in shaping the microbiome associated with Quercus robur woody tissue using culture-independent 16S rRNA gene amplicon sequencing. In addition to providing a baseline survey of the Q. robur microbiome, we screened for the pathogen of acute oak decline. Our results suggest that age is a predictor of bacterial community composition, demonstrating a surprising negative correlation between tree age and alpha diversity. We find no signature of dispersal limitation within the Wytham Woods plot sampled. Together, these results provide evidence for niche-based hypotheses of community assembly and the importance of tree age in bacterial community structure, as well as highlighting that caution must be applied when diagnosing dysbiosis in a long-lived plant host.

Connecting Mobility to Infectious Diseases: The Promise and Limits of Mobile Phone Data.

Human travel can shape infectious disease dynamics by introducing pathogens into susceptible populations or by changing the frequency of contacts between infected and susceptible individuals. Quantifying infectious disease-relevant travel patterns on fine spatial and temporal scales has historically been limited by data availability. The recent emergence of mobile phone calling data and associated locational information means that we can now trace fine scale movement across large numbers of individuals. However, these data necessarily reflect a biased sample of individuals across communities and are generally aggregated for both ethical and pragmatic reasons that may further obscure the nuance of individual and spatial heterogeneities. Additionally, as a general rule, the mobile phone data are not linked to demographic or social identifiers, or to information about the disease status of individual subscribers (although these may be made available in smaller-scale specific cases). Combining data on human movement from mobile phone data-derived population fluxes with data on disease incidence requires approaches that can tackle varying spatial and temporal resolutions of each data source and generate inference about dynamics on scales relevant to both pathogen biology and human ecology. Here, we review the opportunities and challenges of these novel data streams, illustrating our examples with analyses of 2 different pathogens in Kenya, and conclude by outlining core directions for future research.

Understanding Herd Immunity.

Individual immunity is a powerful force affecting host health and pathogen evolution. Importantly, the effects of individual immunity also scale up to affect pathogen transmission dynamics and the success of vaccination campaigns for entire host populations. Population-scale immunity is often termed 'herd immunity'. Here we outline how individual immunity maps to population outcomes and discuss implications for control of infectious diseases. Particular immunological characteristics may be more or less likely to result in a population level signature of herd immunity; we detail this and also discuss other population-level outcomes that might emerge from individual-level immunity.

Quantifying seasonal population fluxes driving rubella transmission dynamics using mobile phone data.

Changing patterns of human aggregation are thought to drive annual and multiannual outbreaks of infectious diseases, but the paucity of data about travel behavior and population flux over time has made this idea difficult to test quantitatively. Current measures of human mobility, especially in low-income settings, are often static, relying on approximate travel times, road networks, or cross-sectional surveys. Mobile phone data provide a unique source of information about human travel, but the power of these data to describe epidemiologically relevant changes in population density remains unclear. Here we quantify seasonal travel patterns using mobile phone data from nearly 15 million anonymous subscribers in Kenya. Using a rich data source of rubella incidence, we show that patterns of population travel (fluxes) inferred from mobile phone data are predictive of disease transmission and improve significantly on standard school term time and weather covariates. Further, combining seasonal and spatial data on travel from mobile phone data allows us to characterize seasonal fluctuations in risk across Kenya and produce dynamic importation risk maps for rubella. Mobile phone data therefore offer a valuable previously unidentified source of data for measuring key drivers of seasonal epidemics.

Seven challenges in modeling vaccine preventable diseases.

Vaccination has been one of the most successful public health measures since the introduction of basic sanitation. Substantial mortality and morbidity reductions have been achieved via vaccination against many infections, and the list of diseases that are potentially controllable by vaccines is growing steadily. We introduce key challenges for modeling in shaping our understanding and guiding policy decisions related to vaccine preventable diseases.

Five challenges in evolution and infectious diseases.

Evolution is a key aspect of the biology of many pathogens, driving processes ranging from immune escape to changes in virulence. Because evolution is inherently subject to feedbacks, and because pathogen evolution plays out at scales ranging from within-host to between-host and beyond, evolutionary questions provide special challenges to the modelling community. In this article, we provide an overview of five challenges in modelling the evolution of pathogens and their hosts, and point to areas for development, focussing in particular on the issue of linking theory and data.