Effect of non-pharmaceutical interventions to contain COVID-19 in China.

On 11 March 2020, the World Health Organization (WHO) declared coronavirus disease 2019 (COVID-19) a pandemic1. The strategies based on non-pharmaceutical interventions that were used to contain the outbreak in China appear to be effective2, but quantitative research is still needed to assess the efficacy of non-pharmaceutical interventions and their timings3. Here, using epidemiological data on COVID-19 and anonymized data on human movement4,5, we develop a modelling framework that uses daily travel networks to simulate different outbreak and intervention scenarios across China. We estimate that there were a total of 114,325 cases of COVID-19 (interquartile range 76,776-164,576) in mainland China as of 29 February 2020. Without non-pharmaceutical interventions, we predict that the number of cases would have been 67-fold higher (interquartile range 44-94-fold) by 29 February 2020, and we find that the effectiveness of different interventions varied. We estimate that early detection and isolation of cases prevented more infections than did travel restrictions and contact reductions, but that a combination of non-pharmaceutical interventions achieved the strongest and most rapid effect. According to our model, the lifting of travel restrictions from 17 February 2020 does not lead to an increase in cases across China if social distancing interventions can be maintained, even at a limited level of an on average 25% reduction in contact between individuals that continues until late April. These findings improve our understanding of the effects of non-pharmaceutical interventions on COVID-19, and will inform response efforts across the world.

Optimizing the detection of emerging infections using mobility-based spatial sampling.

Timely and precise detection of emerging infections is imperative for effective outbreak management and disease control. Human mobility significantly influences the spatial transmission dynamics of infectious diseases. Spatial sampling, integrating the spatial structure of the target, holds promise as an approach for testing allocation in detecting infections, and leveraging information on individuals' movement and contact behavior can enhance targeting precision. This study introduces a spatial sampling framework informed by spatiotemporal analysis of human mobility data, aiming to optimize the allocation of testing resources for detecting emerging infections. Mobility patterns, derived from clustering point-of-interest and travel data, are integrated into four spatial sampling approaches at the community level. We evaluate the proposed mobility-based spatial sampling by analyzing both actual and simulated outbreaks, considering scenarios of transmissibility, intervention timing, and population density in cities. Results indicate that leveraging inter-community movement data and initial case locations, the proposed Case Flow Intensity (CFI) and Case Transmission Intensity (CTI)-informed spatial sampling enhances community-level testing efficiency by reducing the number of individuals screened while maintaining a high accuracy rate in infection identification. Furthermore, the prompt application of CFI and CTI within cities is crucial for effective detection, especially in highly contagious infections within densely populated areas. With the widespread use of human mobility data for infectious disease responses, the proposed theoretical framework extends spatiotemporal data analysis of mobility patterns into spatial sampling, providing a cost-effective solution to optimize testing resource deployment for containing emerging infectious diseases.

A Mixture Model for Estimating SARS-CoV-2 Seroprevalence in Chennai, India.

Serological assays used to estimate the prevalence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) often rely on manufacturers' cutoffs established on the basis of severe cases. We conducted a household-based serosurvey of 4,677 individuals in Chennai, India, from January to May 2021. Samples were tested for SARS-CoV-2 immunoglobulin G (IgG) antibodies to the spike (S) and nucleocapsid (N) proteins. We calculated seroprevalence, defining seropositivity using manufacturer cutoffs and using a mixture model based on measured IgG level. Using manufacturer cutoffs, there was a 5-fold difference in seroprevalence estimated by each assay. This difference was largely reconciled using the mixture model, with estimated anti-S and anti-N IgG seroprevalence of 64.9% (95% credible interval (CrI): 63.8, 66.0) and 51.5% (95% CrI: 50.2, 52.9), respectively. Age and socioeconomic factors showed inconsistent relationships with anti-S and anti-N IgG seropositivity using manufacturer cutoffs. In the mixture model, age was not associated with seropositivity, and improved household ventilation was associated with lower seropositivity odds. With global vaccine scale-up, the utility of the more stable anti-S IgG assay may be limited due to the inclusion of the S protein in several vaccines. Estimates of SARS-CoV-2 seroprevalence using alternative targets must consider heterogeneity in seroresponse to ensure that seroprevalence is not underestimated and correlates are not misinterpreted.

Comparison of allocation strategies of convalescent plasma to reduce excess infections and mortality from SARS-CoV-2 in a US-like population.

BACKGROUND: While the use of convalescent plasma (CP) in the ongoing COVID-19 pandemic has been inconsistent, CP has the potential to reduce excess morbidity and mortality in future pandemics. Given constraints on CP supply, decisions surrounding the allocation of CP must be made. STUDY DESIGN AND METHODS: Using a discrete-time stochastic compartmental model, we simulated implementation of four potential allocation strategies: administering CP to individuals in early hospitalization with COVID-19; administering CP to individuals in outpatient settings; administering CP to hospitalized individuals and administering any remaining CP to outpatient individuals and administering CP in both settings while prioritizing outpatient individuals. We examined the final size of SARS-CoV-2 infections, peak and cumulative hospitalizations, and cumulative deaths under each of the allocation scenarios over a 180-day period. We compared the cost per weighted health benefit under each strategy. RESULTS: Prioritizing administration to patients in early hospitalization, with remaining plasma administered in outpatient settings, resulted in the highest reduction in mortality, averting on average 15% more COVID-19 deaths than administering to hospitalized individuals alone (95% CI [11%-18%]). Prioritizing administration to outpatients, with remaining plasma administered to hospitalized individuals, had the highest percentage of hospitalizations averted (22% [21%-23%] higher than administering to hospitalized individuals alone). DISCUSSION: Convalescent plasma allocation strategy should be determined by the relative priority of averting deaths, infections, or hospitalizations. Under conditions considered, mixed allocation strategies (allocating CP to both outpatient and hospitalized individuals) resulted in a larger percentage of infections and deaths averted than administering CP in a single setting.

Temporal and spatial analysis of Plasmodium falciparum genomics reveals patterns of parasite connectivity in a low-transmission district in Southern Province, Zambia.

BACKGROUND: Understanding temporal and spatial dynamics of malaria transmission will help to inform effective interventions and strategies in regions approaching elimination. Parasite genomics are increasingly used to monitor epidemiologic trends, including assessing residual transmission across seasons and importation of malaria into these regions. METHODS: In a low and seasonal transmission setting of southern Zambia, a total of 441 Plasmodium falciparum samples collected from 8 neighbouring health centres between 2012 and 2018 were genotyped using molecular inversion probes (MIPs n = 1793) targeting a total of 1832 neutral and geographically informative SNPs distributed across the parasite genome. After filtering for quality and missingness, 302 samples and 1410 SNPs were retained and used for downstream population genomic analyses. RESULTS: The analyses revealed most (67%, n = 202) infections harboured one clone (monogenomic) with some variation at local level suggesting low, but heterogenous malaria transmission. Relatedness identity-by-descent (IBD) analysis revealed variable distribution of IBD segments across the genome and 6% of pairs were highly-related (IBD ≥ 0.25). Some of the highly-related parasite populations persisted across multiple seasons, suggesting that persistence of malaria in this low-transmission region is fueled by parasites "seeding" across the dry season. For recent years, clusters of clonal parasites were identified that were dissimilar to the general parasite population, suggesting parasite populations were increasingly fragmented at small spatial scales due to intensified control efforts. Clustering analysis using PCA and t-SNE showed a lack of substantial parasite population structure. CONCLUSION: Leveraging both genomic and epidemiological data provided comprehensive picture of fluctuations in parasite populations in this pre-elimination setting of southern Zambia over 7 years.

A Bayesian hierarchical model for signal extraction from protein microarrays.

Protein microarrays are a promising technology that measure protein levels in serum or plasma samples. Due to their high technical variability and high variation in protein levels across serum samples in any population, directly answering biological questions of interest using protein microarray measurements is challenging. Analyzing preprocessed data and within-sample ranks of protein levels can mitigate the impact of between-sample variation. As for any analysis, ranks are sensitive to preprocessing, but loss function based ranks that accommodate major structural relations and components of uncertainty are very effective. Bayesian modeling with full posterior distributions for quantities of interest produce the most effective ranks. Such Bayesian models have been developed for other assays, for example, DNA microarrays, but modeling assumptions for these assays are not appropriate for protein microarrays. Consequently, we develop and evaluate a Bayesian model to extract the full posterior distribution of normalized protein levels and associated ranks for protein microarrays, and show that it fits well to data from two studies that use protein microarrays produced by different manufacturing processes. We validate the model via simulation and demonstrate the downstream impact of using estimates from this model to obtain optimal ranks.

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.

A pre-processing pipeline to quantify, visualize, and reduce technical variation in protein microarray studies.

Technical variation, or variation from non-biological sources, is present in most laboratory assays. Correcting for this variation enables analysts to extract a biological signal that informs questions of interest. However, each assay has different sources and levels of technical variation, and the choice of correction methods can impact downstream analyses. Compared to similar assays such as DNA microarrays, relatively few methods have been developed and evaluated for protein microarrays, a versatile tool for measuring levels of various proteins in serum samples. Here, we propose a pre-processing pipeline to correct for some common sources of technical variation in protein microarrays. The pipeline builds upon an existing normalization method by using controls to reduce technical variation. We evaluate our method using data from two protein microarray studies and by simulation. We demonstrate that pre-processing choices impact the fluorescent-intensity based ranks of proteins, which in turn, impact downstream analysis.

Improvements in Severe Acute Respiratory Syndrome Coronavirus 2 Testing Cascade in the United States: Data From Serial Cross-sectional Assessments.

BACKGROUND: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) testing is critical for monitoring case counts, early detection and containment of infection, clinical management, and surveillance of variants. However, community-based data on the access, uptake, and barriers to testing have been lacking. METHODS: We conducted serial cross-sectional online surveys covering demographics, coronavirus disease 2019 symptoms, and experiences around SARS-CoV-2 diagnostic testing to characterize the SARS-CoV-2 testing cascade and associated barriers across 10 US states (California, Florida, Illinois, Maryland, Massachusetts, Nebraska, North Dakota, South Dakota, Texas, and Wisconsin), from July 2020 to February 2021. RESULTS: In February 2021, across 10 US states, 895 respondents (11%) reported wanting a diagnostic test in the prior 2 weeks, 63% of whom were tested, with limited variability across states. Almost all (97%) who were tested received their results; 56% received their results within 2 days. In Maryland, Florida, and Illinois, where serial data were available at 4 time points, 56% were tested the same day they wanted or needed a test in February 2021, compared with 28% in July 2020, and 45% received results the same day, compared with 17% in July 2020. Wanting a test was significantly more common among younger, nonwhite respondents and participants with a history of symptoms or exposure. Logistical challenges, including not knowing where to go, were the most frequently cited barriers. CONCLUSIONS: There were significant improvements in access and turnaround times across US states, yet barriers to testing remained consistent across states, underscoring the importance of a continued focus on testing, even amidst mass vaccination campaigns.

Trip duration drives shift in travel network structure with implications for the predictability of spatial disease spread.

Human travel is one of the primary drivers of infectious disease spread. Models of travel are often used that assume the amount of travel to a specific destination decreases as cost of travel increases with higher travel volumes to more populated destinations. Trip duration, the length of time spent in a destination, can also impact travel patterns. We investigated the spatial patterns of travel conditioned on trip duration and find distinct differences between short and long duration trips. In short-trip duration travel networks, trips are skewed towards urban destinations, compared with long-trip duration networks where travel is more evenly spread among locations. Using gravity models to inform connectivity patterns in simulations of disease transmission, we show that pathogens with shorter generation times exhibit initial patterns of spatial propagation that are more predictable among urban locations. Further, pathogens with a longer generation time have more diffusive patterns of spatial spread reflecting more unpredictable disease dynamics.

Identification of factors associated with residual malaria transmission using school-based serological surveys in settings pursuing elimination.

BACKGROUND: Targeted research on residual malaria transmission is important to improve strategies in settings pursuing elimination, where transmission reductions prove challenging. This study aimed to detect and characterize spatial heterogeneity and factors associated with Plasmodium falciparum infections and exposure, P. falciparum apical membrane antigen 1 (PfAMA1) antibody (Ab) response, in the Central Highlands of Madagascar (CHL). METHODS: From May to July 2014, a cross-sectional school-based survey was carried out in 182 fokontany (villages) within 7 health districts of the CHL. Rapid diagnostic tests (RDTs) and a bead-based immunoassay including PfAMA1 antigen biomarker were used to estimate malaria prevalence and seroprevalence, respectively. Local Moran's I index was used to detect spatial "hotspots". Remotely sensed environmental data-temperature, vegetation indices, land covers, and elevation-were used in multivariable mixed-effects logistic regression models to characterize factors associated with malaria infection and cumulative exposure. RESULTS: Among 6,293 school-children ages 2-14 years surveyed, RDT prevalence was low at 0.8% (95% CI 0.6-1.1%), while PfAMA1 Ab seroprevalence was 7.0% (95% CI 6.4-7.7%). Hotspots of PfAMA1 Ab seroprevalence were observed in two districts (Ankazobe and Mandoto). Seroprevalence increased for children living > 5 km from a health centre (adjusted odds ratio (OR) = 1.6, 95% CI 1.2-2.2), and for those experiencing a fever episode in the previous 2 weeks (OR 1.7, 95% CI 1.2-2.4), but decreased at higher elevation (for each 100-m increase, OR = 0.7, 95% CI 0.6-0.8). A clear age pattern was observed whereby children 9-10 years old had an OR of 1.8 (95% CI 1.2-2.4), children 11-12 years an OR of 3.7 (95% CI 2.8-5.0), and children 13-14 years an OR of 5.7 (95% CI 4.0-8.0) for seropositivity, compared with younger children (2-8 years). CONCLUSION: The use of serology in this study provided a better understanding of malaria hotspots and associated factors, revealing a pattern of higher transmission linked to geographical barriers in health care access. The integration of antibody-assays into existing surveillance activities could improve exposure assessment, and may help to monitor the effectiveness of malaria control efforts and adapt elimination interventions.

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.

Prioritizing COVID-19 vaccination efforts and dose allocation within Madagascar.

BACKGROUND: While mass COVID-19 vaccination programs are underway in high-income countries, limited availability of doses has resulted in few vaccines administered in low and middle income countries (LMICs). The COVID-19 Vaccines Global Access (COVAX) is a WHO-led initiative to promote vaccine access equity to LMICs and is providing many of the doses available in these settings. However, initial doses are limited and countries, such as Madagascar, need to develop prioritization schemes to maximize the benefits of vaccination with very limited supplies. There is some consensus that dose deployment should initially target health care workers, and those who are more vulnerable including older individuals. However, questions of geographic deployment remain, in particular associated with limits around vaccine access and delivery capacity in underserved communities, for example in rural areas that may also include substantial proportions of the population. METHODS: To address these questions, we developed a mathematical model of SARS-CoV-2 transmission dynamics and simulated various vaccination allocation strategies for Madagascar. Simulated strategies were based on a number of possible geographical prioritization schemes, testing sensitivity to initial susceptibility in the population, and evaluating the potential of tests for previous infection. RESULTS: Using cumulative deaths due to COVID-19 as the main outcome of interest, our results indicate that distributing the number of vaccine doses according to the number of elderly living in the region or according to the population size results in a greater reduction of mortality compared to distributing doses based on the reported number of cases and deaths. The benefits of vaccination strategies are diminished if the burden (and thus accumulated immunity) has been greatest in the most populous regions, but the overall strategy ranking remains comparable. If rapid tests for prior immunity may be swiftly and effectively delivered, there is potential for considerable gain in mortality averted, but considering delivery limitations modulates this. CONCLUSION: At a subnational scale, our results support the strategy adopted by the COVAX initiative at a global scale.

Rapid Real-time Tracking of Nonpharmaceutical Interventions and Their Association With Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Positivity: The Coronavirus Disease 2019 (COVID-19) Pandemic Pulse Study.

BACKGROUND: Current mitigation strategies for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) rely on the population-wide adoption of nonpharmaceutical interventions (NPIs). Monitoring the adoption of NPIs and their associations with SARS-CoV-2 infection history can provide key information for public health. METHODS: We sampled 1030 individuals in Maryland from 17-28 June 2020 to capture sociodemographically and geographically resolved information about NPI adoption and access to SARS-CoV-2 testing, and examine associations with self-reported SARS-CoV-2 positivity. RESULTS: Overall, 92% reported traveling for essential services and 66% visited friends/family. Use of public transport was reported by 18%. In total, 68% reported strict social distancing indoors and 53% reported strict masking indoors; indoor social distancing was significantly associated with age, and race/ethnicity and income were associated with masking. Overall, 55 participants (5.3%) self-reported ever testing positive for SARS-CoV-2, with strong dose-response relationships between several forms of movement frequency and SARS-CoV-2 positivity. In a multivariable analysis, a history of SARS-CoV-2 infection was negatively associated with strict social distancing (adjusted odds ratio [aOR] for outdoor social distancing, 0.10; 95% confidence interval, .03-.33). Only public transport use (aOR for >7 times vs never, 4.3) and visiting a place of worship (aOR for ≥3 times vs never, 16.0) remained significantly associated with SARS-CoV-2 infection after adjusting for strict social distancing and demographics. CONCLUSIONS: These results support public health messaging that strict social distancing during most activities can reduce SARS-CoV-2 transmission. Additional considerations are needed for indoor activities with large numbers of persons (places of worship and public transportation), where even NPIs may not be possible or sufficient.

The Challenge of Achieving Immunity Through Multiple-Dose Vaccines in Madagascar.

Administration of many childhood vaccines requires that multiple doses be delivered within a narrow time window to provide adequate protection and reduce disease transmission. Accurately quantifying vaccination coverage is complicated by limited individual-level data and multiple vaccination mechanisms (routine and supplementary vaccination programs). We analyzed 12,541 vaccination cards from 6 districts across Madagascar for children born in 2015 and 2016. For 3 vaccines-pentavalent diphtheria-tetanus-pertussis-hepatitis B-Haemophilus influenzae type b vaccine (DTP-HB-Hib; 3 doses), 10-valent pneumococcal conjugate vaccine (PCV10; 3 doses), and rotavirus vaccine (2 doses)-we used dates of vaccination and birth to estimate coverage at 1 year of age and timeliness of delivery. Vaccination coverage at age 1 year for the first dose was consistently high, with decreases for subsequent doses (DTP-HB-Hib: 91%, 81%, and 72%; PCV10: 82%, 74%, and 64%; rotavirus: 73% and 63%). Coverage levels between urban districts and their rural counterparts did not differ consistently. For each dose of DTP-HB-Hib, the overall percentage of individuals receiving late doses was 29%, 7%, and 6%, respectively; estimates were similar for other vaccines. Supplementary vaccination weeks, held to help children who had missed routine care to catch up, did not appear to increase the likelihood of being vaccinated. Maintaining population-level immunity with multiple-dose vaccines requires a robust stand-alone routine immunization program.

Mapping the travel patterns of people with malaria in Bangladesh.

BACKGROUND: Spread of malaria and antimalarial resistance through human movement present major threats to current goals to eliminate the disease. Bordering the Greater Mekong Subregion, southeast Bangladesh is a potentially important route of spread to India and beyond, but information on travel patterns in this area are lacking. METHODS: Using a standardised short survey tool, 2090 patients with malaria were interviewed at 57 study sites in 2015-2016 about their demographics and travel patterns in the preceding 2 months. RESULTS: Most travel was in the south of the study region between Cox's Bazar district (coastal region) to forested areas in Bandarban (31% by days and 45% by nights), forming a source-sink route. Less than 1% of travel reported was between the north and south forested areas of the study area. Farmers (21%) and students (19%) were the top two occupations recorded, with 67 and 47% reporting travel to the forest respectively. Males aged 25-49 years accounted for 43% of cases visiting forests but only 24% of the study population. Children did not travel. Women, forest dwellers and farmers did not travel beyond union boundaries. Military personnel travelled the furthest especially to remote forested areas. CONCLUSIONS: The approach demonstrated here provides a framework for identifying key traveller groups and their origins and destinations of travel in combination with knowledge of local epidemiology to inform malaria control and elimination efforts. Working with the NMEP, the findings were used to derive a set of policy recommendations to guide targeting of interventions for elimination.

Coexisting attractors in the context of cross-scale population dynamics: measles in London as a case study.

Patterns of measles infection in large urban populations have long been considered the paradigm of synchronized nonlinear dynamics. Indeed, recurrent epidemics appear approximately mass-action despite underlying heterogeneity. However, using a subset of rich, newly digitized mortality data (1897-1906), we challenge that proposition. We find that sub-regions of London exhibited a mixture of simultaneous annual and biennial dynamics, while the aggregate city-level dynamics appears firmly annual. Using a simple stochastic epidemic model and maximum-likelihood inference methods, we show that we can capture this observed variation in periodicity. We identify agreement between theory and data, indicating that both changes in periodicity and epidemic coupling between regions can follow relatively simple rules; in particular we find local variation in seasonality drives periodicity. Our analysis underlines that multiple attractors can coexist in a strongly mixed population and follow theoretical predictions.

Long-term dynamics of measles in London: Titrating the impact of wars, the 1918 pandemic, and vaccination.

A key question in ecology is the relative impact of internal nonlinear dynamics and external perturbations on the long-term trajectories of natural systems. Measles has been analyzed extensively as a paradigm for consumer-resource dynamics due to the oscillatory nature of the host-pathogen life cycle, the abundance of rich data to test theory, and public health relevance. The dynamics of measles in London, in particular, has acted as a prototypical test bed for such analysis using incidence data from the pre-vaccination era (1944-1967). However, during this timeframe there were few external large-scale perturbations, limiting an assessment of the relative impact of internal and extra demographic perturbations to the host population. Here, we extended the previous London analyses to include nearly a century of data that also contains four major demographic changes: the First and Second World Wars, the 1918 influenza pandemic, and the start of a measles mass vaccination program. By combining mortality and incidence data using particle filtering methods, we show that a simple stochastic epidemic model, with minimal historical specifications, can capture the nearly 100 years of dynamics including changes caused by each of the major perturbations. We show that the majority of dynamic changes are explainable by the internal nonlinear dynamics of the system, tuned by demographic changes. In addition, the 1918 influenza pandemic and World War II acted as extra perturbations to this basic epidemic oscillator. Our analysis underlines that long-term ecological and epidemiological dynamics can follow very simple rules, even in a non-stationary population subject to significant perturbations and major secular changes.

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.

Genetic Evidence of Focal Plasmodium falciparum Transmission in a Pre-elimination Setting in Southern Province, Zambia.

BACKGROUND: Southern Province, Zambia has experienced a dramatic decline in Plasmodium falciparum malaria transmission in the past decade and is targeted for elimination. Zambia's National Malaria Elimination Program recommends reactive case detection (RCD) within 140 m of index households to enhance surveillance and eliminate remaining transmission foci. METHODS: To evaluate whether RCD captures local transmission, we genotyped 26 microsatellites from 106 samples collected from index (n = 27) and secondary (n = 79) cases detected through RCD in the Macha Hospital catchment area between January 2015 and April 2016. RESULTS: Participants from the same RCD event harbored more genetically related parasites than those from different RCD events, suggesting that RCD captures, at least in part, infections related through local transmission. Related parasites clustered in space and time, up to at least 250 m from index households. Spatial analysis identified a putative focal transmission hotspot. CONCLUSIONS: The current RCD strategy detects focal transmission events, although programmatic guidelines to screen within 140 m of index households may fail to capture all secondary cases. This study highlights the utility of parasite genetic data in assessing programmatic interventions, and similar approaches may be useful to malaria elimination programs seeking to tailor intervention strategies to the underlying transmission epidemiology.