Injection network drivers of HIV prevention service utilization among people who inject drugs: results of a community-based sociometric network cohort in New Delhi, India.

INTRODUCTION: Injection drug networks may influence their network members' health-seeking behaviours. Using data from a sociometric injecting partner network of people who inject drugs (PWID) in New Delhi, India, we assessed the role of injecting partner (alter) behaviours on individual engagement in HIV prevention services. METHODS: We enumerated injecting partner linkages among 2512 PWID using coupon referrals and biometric data from November 2017 to March 2020. Participants completed interviewer-administered questionnaires and provided information on injection behaviours, injecting partners, HIV/hepatitis C (HCV) testing and service engagement. Multilevel multiple-membership models (MMMM) evaluated individual PWID HIV testing, medication for opioid use disorder (MOUD) and syringe service engagement as a function of alter attributes, accounting for membership across multiple ego-networks. Logistic regression models assessed parallel associations among socially proximal injecting peers, defined as PWID ≤3 path length from ego. RESULTS: Median age was 26 years; 99% were male. PWID had median 2 injecting partners and 8 socially proximal peers; 14% reported HIV testing, 33% accessed MOUD and 13% used syringe services 6 months prior. In MMMM analyses, PWID with ≥1 versus 0 injecting partners who received HIV testing were significantly more likely to report HIV testing (adjusted odds ratio [aOR]: 2.27, 95% confidence interval [CI]: 1.68-3.16), MOUD (aOR: 1.99, 95% CI: 1.60-2.53) and syringe service use (aOR: 1.66, 95% CI: 1.21-2.39). We observed similar findings for individual MOUD and syringe service use. Having ≥1 versus 0 HIV-positive partners was associated with decreased HIV testing and MOUD but increased syringe service use (aOR: 1.54, 95% CI: 1.09-2.17). PWID with ≥1 versus 0 socially proximal peers who used non-sterile injection equipment reported increased HIV testing (aOR: 1.39, 95% CI: 1.01-1.92), MOUD (aOR: 1.40, 95% CI: 1.10-1.77) and syringe service use (aOR: 1.82, 95% CI: 1.23-2.68). CONCLUSIONS: We found differential associative relationships between individual HIV prevention service engagement and the health or risk behaviours of direct and indirect alters. Characterizing network exposure beyond direct injecting partnerships provided important context on possible mechanisms of behavioural influence. Findings could be leveraged to design peer-based interventions that promote network diffusion of health-seeking behaviours.

Who is missed in a community-based survey: Assessment and implications of biases due to incomplete sampling frame in a community-based serosurvey, Choma and Ndola Districts, Zambia, 2022.

Community-based serological studies are increasingly relied upon to measure disease burden, identify population immunity gaps, and guide control and elimination strategies; however, there is little understanding of the potential for and impact of sampling biases on outcomes of interest. As part of efforts to quantify measles immunity gaps in Zambia, a community-based serological survey using stratified multi-stage cluster sampling approach was conducted in Ndola and Choma districts in May-June 2022, enrolling 1245 individuals. We carried out a follow-up study among individuals missed from the sampling frame of the serosurvey in July-August 2022, enrolling 672 individuals. We assessed the potential for and impact of biases in the community-based serosurvey by i) estimating differences in characteristics of households and individuals included and excluded (77% vs 23% of households) from the sampling frame of the serosurvey and ii) evaluating the magnitude these differences make on healthcare-seeking behavior, vaccination coverage, and measles seroprevalence. We found that missed households were 20% smaller and 25% less likely to have children. Missed individuals resided in less wealthy households, had different distributions of sex and occupation, and were more likely to seek care at health facilities. Despite these differences, simulating a survey in which missed households were included in the sampling frame resulted in less than a 5% estimated bias in these outcomes. Although community-based studies are upheld as the gold standard study design in assessing immunity gaps and underlying community health characteristics, these findings underscore the fact that sampling biases can impact the results of even well-conducted community-based surveys. Results from these studies should be interpreted in the context of the study methodology and challenges faced during implementation, which include shortcomings in establishing accurate and up-to-date sampling frames. Failure to account for these shortcomings may result in biased estimates and detrimental effects on decision-making.

Characterizing mobility patterns and malaria risk factors in semi-nomadic populations of Northern Kenya.

While many studies have characterized mobility patterns and disease dynamics of settled populations, few have focused on more mobile populations. Highly mobile groups are often at higher disease risk due to their regular movement that may increase the variability of their environments, reduce their access to health care, and limit the number of intervention strategies suitable for their lifestyles. Quantifying the movements and their associated disease risks will be key to developing interventions more suitable for mobile populations. Turkana, Kenya is an ideal setting to characterize these relationships. While the vast, semi-arid county has a large mobile population (>60%) and was recently shown to have endemic malaria, the relationship between mobility and malaria risk in this region has not yet been defined. Here, we worked with 250 semi-nomadic households from four communities in Central Turkana to 1) characterize mobility patterns of travelers and 2) test the hypothesis that semi-nomadic individuals are at greater risk of malaria exposure when migrating with their herds than when staying at their semi-permanent settlements. Participants provided medical and travel histories, demographics, and a dried blood spot for malaria testing before and after the travel period. Further, a subset of travelers was given GPS loggers to document their routes. Four travel patterns emerged from the logger data, Long Term, Transient, Day trip, and Static, with only Long Term and Transient trips being associated with malaria cases detected in individuals who carried GPS devices. After completing their trips, travelers had a higher prevalence of malaria than those who remained at the household (9.2% vs 4.4%), regardless of gender and age. These findings highlight the need to develop intervention strategies amenable to mobile lifestyles that can ultimately help prevent the transmission of malaria.

Network centrality and HIV prevention service use among people who inject drugs: Findings from a sociometric network cohort in New Delhi, India.

BACKGROUND AND AIMS: Network centrality, an indicator of an individual's importance and potential to drive behavioral change, is rarely used to select peer educators. Individual-level predictors of network centrality might be useful to identify people who inject drugs (PWID) for potential roles as peer navigators or change agents in network-based interventions in settings where sociometric data are unavailable. We assessed the relationship between network centrality and HIV prevention service engagement to determine whether centrally-positioned PWID share measurable commonalities. DESIGN: Observational study and survey using baseline data from a sociometric network cohort of PWID, enumerated using network software and biometric data (2017-2020). Network ties corresponded to direct injection partnerships in the prior month. SETTING: New Delhi, India. PARTICIPANTS: A total of 2512 PWID who were ≥18 years, provided written informed consent, and reported illicit injection drug use within the 24 months before study enrollment. MEASUREMENTS: Interviewer-administered questionnaires measured demographics and substance use behaviors. Central versus peripheral network position was categorized using betweenness centrality 75th%ile . Logistic regression was used to estimate adjusted odds ratios (aOR) with 95% confidence intervals (95%CI) between network position and HIV testing, medication for opioid use disorder (MOUD), or syringe service use. Lasso models selected predictors of central network position among 20 covariates detailing demographic, biologic, and substance use information. Predictive accuracy was evaluated using model performance metrics. FINDINGS: Overall, median age was 26 years (interquartile range 22-34); 99% were male; 628 were classified as central. Compared with PWID at the periphery, central PWID were more likely to use MOUD (aOR: 1.59, 95%CI: 1.30-1.94) and syringe services (aOR: 2.91, 95%CI: 2.25, 3.76) in the prior six months. Findings for HIV testing were inconclusive (aOR: 1.30, 95%CI: 1.00-1.69). The lasso variable selector identified several predictors of network centrality: HIV and hepatitis C infection, number of PWID seen in the prior month, injecting heroin and buprenorphine (vs. heroin only) six months prior, sharing injection equipment six months prior, experiencing drug overdose in the past year, and moderate/severe depression (vs. none/mild). Average agreement between model-predicted vs. observed values was 0.75; area under the receiver operator curve was 0.69. CONCLUSIONS: In a socioeconomic network of people who inject drugs (PWID) in New Delhi, India, there are common characteristics among individuals based on their network position (central vs. peripheral) but individual-level predictors have only moderate predictive accuracy. Although central network members appear to be more likely to use HIV prevention services than peripheral network members, their potential as change agents may be limited by other factors that impede their ability to adopt or promote HIV prevention service use.

Characterizing mobility patterns and malaria risk factors in semi-nomadic populations of Northern Kenya.

While many studies have characterized mobility patterns and disease dynamics of individuals from settled populations, few have focused on more mobile populations. Highly mobile groups are often at higher disease risk due to their regular movement that may increase the variability of their environments, reduce their access to health care, and limit the number of intervention strategies suitable for their lifestyles. Quantifying the movements and their associated disease risks will be key to developing intervention strategies more suitable for mobile populations. Here, we worked with four semi-nomadic communities in Central Turkana, Kenya to 1) characterize mobility patterns of travelers from semi-nomadic communities and 2) test the hypothesis that semi-nomadic individuals are at greater risk of exposure to malaria during seasonal migrations than when staying at their semi-permanent settlements. From March-October, 2021, we conducted a study in semi-nomadic households (n=250) where some members traveled with their herd while others remained at the semi-permanent settlement. Participants provided medical and travel histories, demographics, and a dried blood spot for malaria testing before and after the travel period. Further, a subset of travelers was given GPS loggers to document their routes. Four travel patterns emerged from the logger data, Long Term, Transient, Day trip, and Static, with only Long Term and Transient trips being associated with malaria cases detected in individuals who carried GPS devices. After completing their trips, travelers had a higher prevalence of malaria than those who remained at the household (9.2% vs 4.4%), regardless of gender, age group, and catchment area. These findings highlight the need to develop intervention strategies amenable to mobile lifestyles that can ultimately help prevent the transmission of malaria.

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

Background: Timely and precise detection of emerging infections is crucial for effective outbreak management and disease control. Human mobility significantly influences infection risks and transmission dynamics, and spatial sampling is a valuable tool for pinpointing potential infections in specific areas. This study explored spatial sampling methods, informed by various mobility patterns, to optimize the allocation of testing resources for detecting emerging infections. Methods: Mobility patterns, derived from clustering point-of-interest data and travel data, were integrated into four spatial sampling approaches to detect emerging infections at the community level. To evaluate the effectiveness of the proposed mobility-based spatial sampling, we conducted analyses using actual and simulated outbreaks under different scenarios of transmissibility, intervention timing, and population density in cities. Results: By leveraging inter-community movement data and initial case locations, the proposed case flow intensity (CFI) and case transmission intensity (CTI)-informed sampling approaches could considerably reduce the number of tests required for both actual and simulated outbreaks. Nonetheless, the prompt use of CFI and CTI within communities is imperative for effective detection, particularly for highly contagious infections in densely populated areas. Conclusions: The mobility-based spatial sampling approach can substantially improve the efficiency of community-level testing for detecting emerging infections. It achieves this by reducing the number of individuals screened while maintaining a high accuracy rate of infection identification. It represents a cost-effective solution to optimize the deployment of testing resources, when necessary, to contain emerging infectious diseases in diverse settings.

Changes in mobility patterns during the COVID-19 pandemic in Zambia: Implications for the effectiveness of NPIs in Sub-Saharan Africa.

The COVID-19 pandemic has impacted many facets of human behavior, including human mobility partially driven by the implementation of non-pharmaceutical interventions (NPIs) such as stay at home orders, travel restrictions, and workplace and school closures. Given the importance of human mobility in the transmission of SARS-CoV-2, there have been an increase in analyses of mobility data to understand the COVID-19 pandemic to date. However, despite an abundance of these analyses, few have focused on Sub-Saharan Africa (SSA). Here, we use mobile phone calling data to provide a spatially refined analysis of sub-national human mobility patterns during the COVID-19 pandemic from March 2020-July 2021 in Zambia using transmission and mobility models. Overall, among highly trafficked intra-province routes, mobility decreased up to 52% during the time of the strictest NPIs (March-May 2020) compared to baseline. However, despite dips in mobility during the first wave of COVID-19 cases, mobility returned to baseline levels and did not drop again suggesting COVID-19 cases did not influence mobility in subsequent waves.

Novel bioinformatic methods and machine learning approaches reveal candidate biomarkers of the intensity and timing of past exposure to Plasmodium falciparum.

Accurately quantifying the burden of malaria over time is an important goal of malaria surveillance efforts and can enable effective targeting and evaluation of interventions. Malaria surveillance methods capture active or recent infections which poses several challenges to achieving malaria surveillance goals. In high transmission settings, asymptomatic infections are common and therefore accurate measurement of malaria burden demands active surveillance; in low transmission regions where infections are rare accurate surveillance requires sampling large subsets of the population; and in any context monitoring malaria burden over time necessitates serial sampling. Antibody responses to Plasmodium falciparum parasites persist after infection and therefore measuring antibodies has the potential to overcome several of the current obstacles to accurate malaria surveillance. Identifying which antibody responses are markers of the timing and intensity of past exposure to P. falciparum remains challenging, particularly among adults who tend to be re-exposed multiple times over the course of their lifetime and therefore have similarly high antibody responses to many Plasmodium antigens. A previous analysis of 479 serum samples from individuals in three regions in southern Africa with different historical levels of P. falciparum malaria transmission (high, intermediate, and low) revealed regional differences in antibody responses to P. falciparum antigens among children under 5 years of age. Using a novel bioinformatic pipeline optimized for protein microarrays that minimizes between-sample technical variation, we used antibody responses to Plasmodium antigens as predictors in random forest models to classify samples from adults into these three regions of differing historical malaria transmission with high accuracy (AUC = 0.99). Many of the most important antigens for classification in these models do not overlap with previously published results and are therefore novel candidate markers for the timing and intensity of past exposure to P. falciparum. Measuring antibody responses to these antigens could lead to improved malaria surveillance.

Effects of public-health measures for zeroing out different SARS-CoV-2 variants.

Targeted public health interventions for an emerging epidemic are essential for preventing pandemics. During 2020-2022, China invested significant efforts in strict zero-COVID measures to contain outbreaks of varying scales caused by different SARS-CoV-2 variants. Based on a multi-year empirical dataset containing 131 outbreaks observed in China from April 2020 to May 2022 and simulated scenarios, we ranked the relative intervention effectiveness by their reduction in instantaneous reproduction number. We found that, overall, social distancing measures (38% reduction, 95% prediction interval 31-45%), face masks (30%, 17-42%) and close contact tracing (28%, 24-31%) were most effective. Contact tracing was crucial in containing outbreaks during the initial phases, while social distancing measures became increasingly prominent as the spread persisted. In addition, infections with higher transmissibility and a shorter latent period posed more challenges for these measures. Our findings provide quantitative evidence on the effects of public-health measures for zeroing out emerging contagions in different contexts.

Fine scale human mobility changes within 26 US cities in 2020 in response to the COVID-19 pandemic were associated with distance and income.

Human mobility patterns changed greatly due to the COVID-19 pandemic. Despite many analyses investigating general mobility trends, there has been less work characterising changes in mobility on a fine spatial scale and developing frameworks to model these changes. We analyse zip code-level within-city mobility data from 26 US cities between February 2 -August 31, 2020. We use Bayesian models to characterise the initial decrease in mobility and mobility patterns between June-August at this fine spatial scale. There were similar temporal trends across cities but large variations in the magnitude of mobility reductions. Long-distance routes and higher-income subscribers, but not age, were associated with greater mobility reductions. At the city level, mobility rates around early April, when mobility was lowest, and over summer showed little association with non-pharmaceutical interventions or case rates. Changes in mobility patterns lasted until the end of the study period, despite overall numbers of trips recovering to near baseline levels in many cities.

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.

Declining Age-Specific Seroprevalence and Seroconversion Rates in Plasmodium falciparum from 2009 to 2018 Documents Progress toward Malaria Elimination in Southern Zambia.

Obtaining accurate malaria surveillance data is challenging in low-transmission settings because large sample sizes are required to estimate incidence and prevalence precisely. Serology is an additional tool to document progress toward malaria elimination. An enzyme immunoassay to Plasmodium falciparum lysate was used to estimate age-specific seroprevalence among residents of southern Zambia, where malaria transmission has declined to pre-elimination levels during the past two decades. Plasma was eluted from 3,362 dried blood spots collected during five cross-sectional surveys conducted between 2009 and 2012, and again in 2018. Annual seroconversion rates (SCRs), an estimate of the force of infection, were calculated using a reversible catalytic model. The SCR decreased by two thirds from a level of approximately 0.15/year in 2009 and 2010 to approximately 0.05/year in 2011 and 2012, and then decreased 5-fold to 0.01/year by 2018, demonstrating the utility of serology in documenting progress toward elimination.

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.

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.

National-scale simulation of human movement in a spatially coupled individual-based model of malaria in Burkina Faso.

Malaria due to the Plasmodium falciparum parasite remains a threat to human health despite eradication efforts and the development of anti-malarial treatments, such as artemisinin combination therapies. Human movement and migration have been linked to the propagation of malaria on national scales, highlighting the need for the incorporation of human movement in modeling efforts. Spatially couped individual-based models have been used to study how anti-malarial resistance evolves and spreads in response to drug policy changes; however, as the spatial scale of the model increases, the challenges associated with modeling of movement also increase. In this paper we discuss the development, calibration, and validation of a movement model in the context of a national-scale, spatial, individual-based model used to study the evolution of drug resistance in the malaria parasite.

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.

Exploring how space, time, and sampling impact our ability to measure genetic structure across Plasmodium falciparum populations.

A primary use of malaria parasite genomics is identifying highly related infections to quantify epidemiological, spatial, or temporal factors associated with patterns of transmission. For example, spatial clustering of highly related parasites can indicate foci of transmission and temporal differences in relatedness can serve as evidence for changes in transmission over time. However, for infections in settings of moderate to high endemicity, understanding patterns of relatedness is compromised by complex infections, overall high forces of infection, and a highly diverse parasite population. It is not clear how much these factors limit the utility of using genomic data to better understand transmission in these settings. In particular, further investigation is required to determine which patterns of relatedness we expect to see with high quality, densely sampled genomic data in a high transmission setting and how these observations change under different study designs, missingness, and biases in sample collection. Here we investigate two identity-by-state measures of relatedness and apply them to amplicon deep sequencing data collected as part of a longitudinal cohort in Western Kenya that has previously been analysed to identify individual-factors associated with sharing parasites with infected mosquitoes. With these data we use permutation tests, to evaluate several hypotheses about spatiotemporal patterns of relatedness compared to a null distribution. We observe evidence of temporal structure, but not of fine-scale spatial structure in the cohort data. To explore factors associated with the lack of spatial structure in these data, we construct a series of simplified simulation scenarios using an agent based model calibrated to entomological, epidemiological and genomic data from this cohort study to investigate whether the lack of spatial structure observed in the cohort could be due to inherent power limitations of this analytical method. We further investigate how our hypothesis testing behaves under different sampling schemes, levels of completely random and systematic missingness, and different transmission intensities.

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.

Fine scale human mobility changes in 26 US cities in 2020 in response to the COVID-19 pandemic were associated with distance and income.

Human mobility patterns changed greatly due to the COVID-19 pandemic. Despite many analyses investigating general mobility trends, there has been less work characterising changes in mobility on a fine spatial scale and developing frameworks to model these changes. We analyse zip code-level mobility data from 26 US cities between February 2 â€" August 31, 2020. We use Bayesian models to characterise the initial decrease in mobility and mobility patterns between June - August at this fine spatial scale. There were similar temporal trends across cities but large variations in the magnitude of mobility reductions. Long-distance routes and higher-income subscribers, but not age, were associated with greater mobility reductions. At the city level, mobility rates around early April, when mobility was lowest, and over summer showed little association with non-pharmaceutical interventions or case rates. Changes in mobility patterns lasted until the end of the study period, despite overall numbers of trips recovering to near baseline levels in many cities.

Scientific Findings of the Southern and Central Africa International Center of Excellence for Malaria Research: Ten Years of Malaria Control Impact Assessments in Hypo-, Meso-, and Holoendemic Transmission Zones in Zambia and Zimbabwe.

For a decade, the Southern and Central Africa International Center of Excellence for Malaria Research has operated with local partners across study sites in Zambia and Zimbabwe that range from hypo- to holoendemic and vary ecologically and entomologically. The burden of malaria and the impact of control measures were assessed in longitudinal cohorts, cross-sectional surveys, passive and reactive case detection, and other observational designs that incorporated multidisciplinary scientific approaches: classical epidemiology, geospatial science, serosurveillance, parasite and mosquito genetics, and vector bionomics. Findings to date have helped elaborate the patterns and possible causes of sustained low-to-moderate transmission in southern Zambia and eastern Zimbabwe and recalcitrant high transmission and fatality in northern Zambia. Cryptic and novel mosquito vectors, asymptomatic parasite reservoirs in older children, residual parasitemia and gametocytemia after treatment, indoor residual spraying timed dyssynchronously to vector abundance, and stockouts of essential malaria commodities, all in the context of intractable rural poverty, appear to explain the persistent malaria burden despite current interventions. Ongoing studies of high-resolution transmission chains, parasite population structures, long-term malaria periodicity, and molecular entomology are further helping to lay new avenues for malaria control in southern and central Africa and similar settings.