serosim: An R package for simulating serological data arising from vaccination, epidemiological and antibody kinetics processes.

serosim is an open-source R package designed to aid inference from serological studies, by simulating data arising from user-specified vaccine and antibody kinetics processes using a random effects model. Serological data are used to assess population immunity by directly measuring individuals' antibody titers. They uncover locations and/or populations which are susceptible and provide evidence of past infection or vaccination to help inform public health measures and surveillance. Both serological data and new analytical techniques used to interpret them are increasingly widespread. This creates a need for tools to simulate serological studies and the processes underlying observed titer values, as this will enable researchers to identify best practices for serological study design, and provide a standardized framework to evaluate the performance of different inference methods. serosim allows users to specify and adjust model inputs representing underlying processes responsible for generating the observed titer values like time-varying patterns of infection and vaccination, population demography, immunity and antibody kinetics, and serological sampling design in order to best represent the population and disease system(s) of interest. This package will be useful for planning sampling design of future serological studies, understanding determinants of observed serological data, and validating the accuracy and power of new statistical methods.

No evidence of sustained nonzoonotic Plasmodium knowlesi transmission in Malaysia from modelling malaria case data.

Reported incidence of the zoonotic malaria Plasmodium knowlesi has markedly increased across Southeast Asia and threatens malaria elimination. Nonzoonotic transmission of P. knowlesi has been experimentally demonstrated, but it remains unknown whether nonzoonotic transmission is contributing to increases in P. knowlesi cases. Here, we adapt model-based inference methods to estimate RC, individual case reproductive numbers, for P. knowlesi, P. falciparum and P. vivax human cases in Malaysia from 2012-2020 (n = 32,635). Best fitting models for P. knowlesi showed subcritical transmission (RC < 1) consistent with a large reservoir of unobserved infection sources, indicating P. knowlesi remains a primarily zoonotic infection. In contrast, sustained transmission (RC > 1) was estimated historically for P. falciparum and P. vivax, with declines in RC estimates observed over time consistent with local elimination. Together, this suggests sustained nonzoonotic P. knowlesi transmission is highly unlikely and that new approaches are urgently needed to control spillover risks.

SARS-CoV-2 Serology Across Scales: A Framework for Unbiased Estimation of Cumulative Incidence Incorporating Antibody Kinetics and Epidemic Recency.

Serosurveys are a key resource for measuring severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) population exposure. A growing body of evidence suggests that asymptomatic and mild infections (together making up over 95% of all infections) are associated with lower antibody titers than severe infections. Antibody levels also peak a few weeks after infection and decay gradually. We developed a statistical approach to produce estimates of cumulative incidence from raw seroprevalence survey results that account for these sources of spectrum bias. We incorporate data on antibody responses on multiple assays from a postinfection longitudinal cohort, along with epidemic time series to account for the timing of a serosurvey relative to how recently individuals may have been infected. We applied this method to produce estimates of cumulative incidence from 5 large-scale SARS-CoV-2 serosurveys across different settings and study designs. We identified substantial differences between raw seroprevalence and cumulative incidence of over 2-fold in the results of some surveys, and we provide a tool for practitioners to generate cumulative incidence estimates with preset or custom parameter values. While unprecedented efforts have been launched to generate SARS-CoV-2 seroprevalence estimates over this past year, interpretation of results from these studies requires properly accounting for both population-level epidemiologic context and individual-level immune dynamics.

Using sero-epidemiology to monitor disparities in vaccination and infection with SARS-CoV-2.

As SARS-CoV-2 continues to spread and vaccines are rolled-out, the "double burden" of disparities in exposure and vaccination intersect to determine patterns of infection, immunity, and mortality. Serology provides a unique opportunity to measure prior infection and vaccination simultaneously. Leveraging algorithmically-selected residual sera from two hospital networks in the city of San Francisco, cross-sectional samples from 1,014 individuals from February 4-17, 2021 were each tested on two assays (Ortho Clinical Diagnostics VITROS Anti-SARS-CoV-2 and Roche Elecsys Anti-SARS-CoV-2), capturing the first year of the epidemic and early roll-out of vaccination. We estimated, using Bayesian estimation of infection and vaccination, that infection risk of Hispanic/Latinx residents was five times greater than of White residents aged 18-64 (95% Credible Interval (CrI): 3.2-10.3), and that White residents over 65 were twice as likely to be vaccinated as Black/African American residents (95% CrI: 1.1-4.6). We found that socioeconomically-deprived zipcodes had higher infection probabilities and lower vaccination coverage than wealthier zipcodes. While vaccination has created a 'light at the end of the tunnel' for this pandemic, ongoing challenges in achieving and maintaining equity must also be considered.

Novel Epidemic Metrics to Communicate Outbreak Risk at the Municipality Level: Dengue and Zika in the Dominican Republic.

Arboviruses remain a significant cause of morbidity, mortality and economic cost across the global human population. Epidemics of arboviral disease, such as Zika and dengue, also cause significant disruption to health services at local and national levels. This study examined 2014-2016 Zika and dengue epidemic data at the sub-national level to characterise transmission across the Dominican Republic. For each municipality, spatio-temporal mapping was used to characterise disease burden, while data were age and sex standardised to quantify burden distributions among the population. In separate analyses, time-ordered data were combined with the underlying disease migration interval distribution to produce a network of likely transmission chain events, displayed using transmission chain likelihood matrices. Finally, municipal-specific reproduction numbers (Rm) were established using a Wallinga-Teunis matrix. Dengue and Zika epidemics peaked during weeks 39-52 of 2015 and weeks 14-27 of 2016, respectively. At the provincial level, dengue attack rates were high in Hermanas Mirabal and San José de Ocoa (58.1 and 49.2 cases per 10,000 population, respectively), compared with the Zika burden, which was highest in Independencia and San José de Ocoa (21.2 and 13.4 cases per 10,000 population, respectively). Across municipalities, high disease burden was observed in Cotuí (622 dengue cases per 10,000 population) and Jimani (32 Zika cases per 10,000 population). Municipal infector-infectee transmission likelihood matrices identified seven 0% likelihood transmission events throughout the dengue epidemic and two 0% likelihood transmission events during the Zika epidemic. Municipality reproduction numbers (Rm) were consistently higher, and persisted for a greater duration, during the Zika epidemic (Rm = 1.0) than during the dengue epidemic (Rm < 1.0). This research highlights the importance of disease surveillance in land border municipalities as an early warning for infectious disease transmission. It also demonstrates that a high number of importation events are required to sustain transmission in endemic settings, and vice versa for newly emerged diseases. The inception of a novel epidemiological metric, Rm, reports transmission risk using standardised spatial units, and can be used to identify high transmission risk municipalities to better focus public health interventions for dengue, Zika and other infectious diseases.

Using sero-epidemiology to monitor disparities in vaccination and infection with SARS-CoV-2.

BACKGROUND: As COVID-19 vaccines continue to be rolled-out, the "double burden" of health disparities in both exposure to infection and vaccination coverage intersect to determine the current and future patterns of infection, immunity, and mortality. Serology provides a unique opportunity to measure biomarkers of infection and vaccination simultaneously, and to relate these metrics to demographic and geographic factors. METHODS: Leveraging algorithmically selected residual serum samples from two hospital networks in San Francisco, we sampled 1014 individuals during February 2021, capturing transmission during the first 11 months of the epidemic and the early roll out of vaccination. These samples were tested using two serologic assays: one detecting antibodies elicited by infection, and not by vaccines, and one detecting antibodies elicited by both infection and vaccination. We used Bayesian statistical models to estimate the proportion of the population that was naturally infected and the proportion protected due to vaccination. FINDINGS: We estimated that the risk of prior infection of Latinx residents was 5.3 (95% CI: 3.2 - 10.3) times greater than the risk of white residents aged 18-64 and that white San Francisco residents over the age of 65 were twice as likely (2.0, 95% CI: 1.1 - 4.6) to be vaccinated as Black residents. We also found socioeconomically deprived zipcodes in the city had high probabilities of natural infections and lower vaccination coverage than wealthier zipcodes. INTERPRETATION: Using a platform we created for SARS-CoV-2 serologic data collection in San Francisco, we characterized and quantified the stark disparities in infection rates and vaccine coverage by demographic groups over the first year of the pandemic. While the arrival of the SARS-CoV-2 vaccine has created a 'light at the end of the tunnel' for this pandemic, ongoing challenges in achieving and maintaining equity must also be considered. FUNDING: NIH, NIGMS, Schmidt Science Fellows in partnership with the Rhodes Trust and the Chan Zuckerberg Biohub.

Associations between environmental covariates and temporal changes in malaria incidence in high transmission settings of Uganda: a distributed lag nonlinear analysis.

BACKGROUND: Environmental factors such as temperature, rainfall, and vegetation cover play a critical role in malaria transmission. However, quantifying the relationships between environmental factors and measures of disease burden relevant for public health can be complex as effects are often non-linear and subject to temporal lags between when changes in environmental factors lead to changes in malaria incidence. The study investigated the effect of environmental covariates on malaria incidence in high transmission settings of Uganda. METHODS: This study leveraged data from seven malaria reference centres (MRCs) located in high transmission settings of Uganda over a 24-month period. Estimates of monthly malaria incidence (MI) were derived from MRCs' catchment areas. Environmental data including monthly temperature, rainfall, and normalized difference vegetation index (NDVI) were obtained from remote sensing sources. A distributed lag nonlinear model was used to investigate the effect of environmental covariates on malaria incidence. RESULTS: Overall, the median (range) monthly temperature was 30 °C (26-47), rainfall 133.0 mm (3.0-247), NDVI 0.66 (0.24-0.80) and MI was 790 per 1000 person-years (73-3973). Temperature of 35 °C was significantly associated with malaria incidence compared to the median observed temperature (30 °C) at month lag 2 (IRR: 2.00, 95% CI: 1.42-2.83) and the increased cumulative IRR of malaria at month lags 1-4, with the highest cumulative IRR of 8.16 (95% CI: 3.41-20.26) at lag-month 4. Rainfall of 200 mm significantly increased IRR of malaria compared to the median observed rainfall (133 mm) at lag-month 0 (IRR: 1.24, 95% CI: 1.01-1.52) and the increased cumulative IRR of malaria at month lags 1-4, with the highest cumulative IRR of 1.99(95% CI: 1.22-2.27) at lag-month 4. Average NVDI of 0.72 significantly increased the cumulative IRR of malaria compared to the median observed NDVI (0.66) at month lags 2-4, with the highest cumulative IRR of 1.57(95% CI: 1.09-2.25) at lag-month 4. CONCLUSIONS: In high-malaria transmission settings, high values of environmental covariates were associated with increased cumulative IRR of malaria, with IRR peaks at variable lag times. The complex associations identified are valuable for designing strategies for early warning, prevention, and control of seasonal malaria surges and epidemics.

SARS-CoV-2 serology across scales: a framework for unbiased seroprevalence estimation incorporating antibody kinetics and epidemic recency.

Serosurveys are a key resource for measuring SARS-CoV-2 cumulative incidence. A growing body of evidence suggests that asymptomatic and mild infections (together making up over 95% of all infections) are associated with lower antibody titers than severe infections. Antibody levels also peak a few weeks after infection and decay gradually. We developed a statistical approach to produce adjusted estimates of seroprevalence from raw serosurvey results that account for these sources of spectrum bias. We incorporate data on antibody responses on multiple assays from a post-infection longitudinal cohort, along with epidemic time series to account for the timing of a serosurvey relative to how recently individuals may have been infected. We applied this method to produce adjusted seroprevalence estimates from five large-scale SARS-CoV-2 serosurveys across different settings and study designs. We identify substantial differences between reported and adjusted estimates of over two-fold in the results of some surveys, and provide a tool for practitioners to generate adjusted estimates with pre-set or custom parameter values. While unprecedented efforts have been launched to generate SARS-CoV-2 seroprevalence estimates over this past year, interpretation of results from these studies requires properly accounting for both population-level epidemiologic context and individual-level immune dynamics.

Inference of malaria reproduction numbers in three elimination settings by combining temporal data and distance metrics.

Individual-level geographic information about malaria cases, such as the GPS coordinates of residence or health facility, is often collected as part of surveillance in near-elimination settings, but could be more effectively utilised to infer transmission dynamics, in conjunction with additional information such as symptom onset time and genetic distance. However, in the absence of data about the flow of parasites between populations, the spatial scale of malaria transmission is often not clear. As a result, it is important to understand the impact of varying assumptions about the spatial scale of transmission on key metrics of malaria transmission, such as reproduction numbers. We developed a method which allows the flexible integration of distance metrics (such as Euclidian distance, genetic distance or accessibility matrices) with temporal information into a single inference framework to infer malaria reproduction numbers. Twelve scenarios were defined, representing different assumptions about the likelihood of transmission occurring over different geographic distances and likelihood of missing infections (as well as high and low amounts of uncertainty in this estimate). These scenarios were applied to four individual level datasets from malaria eliminating contexts to estimate individual reproduction numbers and how they varied over space and time. Model comparison suggested that including spatial information improved models as measured by second order AIC (ΔAICc), compared to time only results. Across scenarios and across datasets, including spatial information tended to increase the seasonality of temporal patterns in reproduction numbers and reduced noise in the temporal distribution of reproduction numbers. The best performing parameterisations assumed long-range transmission (> 200 km) was possible. Our approach is flexible and provides the potential to incorporate other sources of information which can be converted into distance or adjacency matrices such as travel times or molecular markers.

Citywide serosurveillance of the initial SARS-CoV-2 outbreak in San Francisco using electronic health records.

Serosurveillance provides a unique opportunity to quantify the proportion of the population that has been exposed to pathogens. Here, we developed and piloted Serosurveillance for Continuous, ActionabLe Epidemiologic Intelligence of Transmission (SCALE-IT), a platform through which we systematically tested remnant samples from routine blood draws in two major hospital networks in San Francisco for SARS-CoV-2 antibodies during the early months of the pandemic. Importantly, SCALE-IT allows for algorithmic sample selection and rich data on covariates by leveraging electronic health record data. We estimated overall seroprevalence at 4.2%, corresponding to a case ascertainment rate of only 4.9%, and identified important heterogeneities by neighborhood, homelessness status, and race/ethnicity. Neighborhood seroprevalence estimates from SCALE-IT were comparable to local community-based surveys, while providing results encompassing the entire city that have been previously unavailable. Leveraging this hybrid serosurveillance approach has strong potential for application beyond this local context and for diseases other than SARS-CoV-2.

Using Hawkes Processes to model imported and local malaria cases in near-elimination settings.

Developing new methods for modelling infectious diseases outbreaks is important for monitoring transmission and developing policy. In this paper we propose using semi-mechanistic Hawkes Processes for modelling malaria transmission in near-elimination settings. Hawkes Processes are well founded mathematical methods that enable us to combine the benefits of both statistical and mechanistic models to recreate and forecast disease transmission beyond just malaria outbreak scenarios. These methods have been successfully used in numerous applications such as social media and earthquake modelling, but are not yet widespread in epidemiology. By using domain-specific knowledge, we can both recreate transmission curves for malaria in China and Eswatini and disentangle the proportion of cases which are imported from those that are community based.

Citywide serosurveillance of the initial SARS-CoV-2 outbreak in San Francisco.

Serosurveillance provides a unique opportunity to quantify the proportion of the population that has been exposed to pathogens. Here, we developed and piloted Serosurveillance for Continuous, ActionabLe Epidemiologic Intelligence of Transmission (SCALE-IT), a platform through which we systematically tested remnant samples from routine blood draws in two major hospital networks in San Francisco for SARS-CoV-2 antibodies during the early months of the pandemic. Importantly, SCALE-IT allows for algorithmic sample selection and rich data on covariates by leveraging electronic medical record data. We estimated overall seroprevalence at 4.2%, corresponding to a case ascertainment rate of only 4.9%, and identified important heterogeneities by neighborhood, homelessness status, and race/ethnicity. Neighborhood seroprevalence estimates from SCALE-IT were comparable to local community-based surveys, while providing results encompassing the entire city that have been previously unavailable. Leveraging this hybrid serosurveillance approach has strong potential for application beyond this local context and for diseases other than SARS-CoV-2.

Seroepidemiologic Study Designs for Determining SARS-COV-2 Transmission and Immunity.

Serologic studies are crucial for clarifying dynamics of the coronavirus disease pandemic. Past work on serologic studies (e.g., during influenza pandemics) has made relevant contributions, but specific conditions of the current situation require adaptation. Although detection of antibodies to measure exposure, immunity, or both seems straightforward conceptually, numerous challenges exist in terms of sample collection, what the presence of antibodies actually means, and appropriate analysis and interpretation to account for test accuracy and sampling biases. Successful deployment of serologic studies depends on type and performance of serologic tests, population studied, use of adequate study designs, and appropriate analysis and interpretation of data. We highlight key questions that serologic studies can help answer at different times, review strengths and limitations of different assay types and study designs, and discuss methods for rapid sharing and analysis of serologic data to determine global transmission of severe acute respiratory syndrome coronavirus 2.

Tracking progress towards malaria elimination in China: Individual-level estimates of transmission and its spatiotemporal variation using a diffusion network approach.

In order to monitor progress towards malaria elimination, it is crucial to be able to measure changes in spatio-temporal transmission. However, common metrics of malaria transmission such as parasite prevalence are under powered in elimination contexts. China has achieved major reductions in malaria incidence and is on track to eliminate, having reporting zero locally-acquired malaria cases in 2017 and 2018. Understanding the spatio-temporal pattern underlying this decline, especially the relationship between locally-acquired and imported cases, can inform efforts to maintain elimination and prevent re-emergence. This is particularly pertinent in Yunnan province, where the potential for local transmission is highest. Using a geo-located individual-level dataset of cases recorded in Yunnan province between 2011 and 2016, we introduce a novel Bayesian framework to model a latent diffusion process and estimate the joint likelihood of transmission between cases and the number of cases with unobserved sources of infection. This is used to estimate the case reproduction number, Rc. We use these estimates within spatio-temporal geostatistical models to map how transmission varied over time and space, estimate the timeline to elimination and the risk of resurgence. We estimate the mean Rc between 2011 and 2016 to be 0.171 (95% CI = 0.165, 0.178) for P. vivax cases and 0.089 (95% CI = 0.076, 0.103) for P. falciparum cases. From 2014 onwards, no cases were estimated to have a Rc value above one. An unobserved source of infection was estimated to be moderately likely (p>0.5) for 19/ 611 cases and high (p>0.8) for 2 cases, suggesting very high levels of case ascertainment. Our estimates suggest that, maintaining current intervention efforts, Yunnan is unlikely to experience sustained local transmission up to 2020. However, even with a mean of 0.005 projected up to 2020, locally-acquired cases are possible due to high levels of importation.

Changing epidemiology and challenges of malaria in China towards elimination.

BACKGROUND: Historically, malaria had been a widespread disease in China. A national plan was launched in China in 2010, aiming to eliminate malaria by 2020. In 2017, no indigenous cases of malaria were detected in China for the first time. To provide evidence for precise surveillance and response to achieve elimination goal, a comprehensive study is needed to determine the changing epidemiology of malaria and the challenges towards elimination. METHODS: Using malaria surveillance data from 2011 to 2016, an integrated series of analyses was conducted to elucidate the changing epidemiological features of autochthonous and imported malaria, and the spatiotemporal patterns of malaria importation from endemic countries. RESULTS: From 2011 to 2016, a total of 21,062 malaria cases with 138 deaths were reported, including 91% were imported and 9% were autochthonous. The geographic distribution of local transmission have shrunk dramatically, but there were still more than 10 counties reporting autochthonous cases in 2013-2016, particularly in counties bordering with countries in South-East Asia. The importation from 68 origins countries had an increasing annual trend from Africa but decreasing importation from Southeast Asia. Four distinct communities have been identified in the importation networks with the destinations in China varied by origin and species. CONCLUSIONS: China is on the verge of malaria elimination, but the residual transmission in border regions and the threats of importation from Africa and Southeast Asia are the key challenges to achieve and maintain malaria elimination. Efforts from China are also needed to help malaria control in origin countries and reduce the risk of introduced transmission.

Estimating spatiotemporally varying malaria reproduction numbers in a near elimination setting.

In 2016 the World Health Organization identified 21 countries that could eliminate malaria by 2020. Monitoring progress towards this goal requires tracking ongoing transmission. Here we develop methods that estimate individual reproduction numbers and their variation through time and space. Individual reproduction numbers, Rc, describe the state of transmission at a point in time and differ from mean reproduction numbers, which are averages of the number of people infected by a typical case. We assess elimination progress in El Salvador using data for confirmed cases of malaria from 2010 to 2016. Our results demonstrate that whilst the average number of secondary malaria cases was below one (0.61, 95% CI 0.55-0.65), individual reproduction numbers often exceeded one. We estimate a decline in Rc between 2010 and 2016. However we also show that if importation is maintained at the same rate, the country may not achieve malaria elimination by 2020.

Modelling the impact of larviciding on the population dynamics and biting rates of Simulium damnosum (s.l.): implications for vector control as a complementary strategy for onchocerciasis elimination in Africa.

BACKGROUND: In 2012, the World Health Organization set goals for the elimination of onchocerciasis transmission by 2020 in selected African countries. Epidemiological data and mathematical modelling have indicated that elimination may not be achieved with annual ivermectin distribution in all endemic foci. Complementary and alternative treatment strategies (ATS), including vector control, will be necessary. Implementation of vector control will require that the ecology and population dynamics of Simulium damnosum (sensu lato) be carefully considered. METHODS: We adapted our previous SIMuliid POPulation dynamics (SIMPOP) model to explore the impact of larvicidal insecticides on S. damnosum (s.l.) biting rates in different ecological contexts and to identify how frequently and for how long vector control should be continued to sustain substantive reductions in vector biting. SIMPOP was fitted to data from large-scale aerial larviciding trials in savannah sites (Ghana) and small-scale ground larviciding trials in forest areas (Cameroon). The model was validated against independent data from Burkina Faso/Côte d'Ivoire (savannah) and Bioko (forest). Scenario analysis explored the effects of ecological and programmatic factors such as pre-control daily biting rate (DBR) and larviciding scheme design on reductions and resurgences in biting rates. RESULTS: The estimated efficacy of large-scale aerial larviciding in the savannah was greater than that of ground-based larviciding in the forest. Small changes in larvicidal efficacy can have large impacts on intervention success. At 93% larvicidal efficacy (a realistic value based on field trials), 10 consecutive weekly larvicidal treatments would reduce DBRs by 96% (e.g. from 400 to 16 bites/person/day). At 70% efficacy, and for 10 weekly applications, the DBR would decrease by 67% (e.g. from 400 to 132 bites/person/day). Larviciding is more likely to succeed in areas with lower water temperatures and where blackfly species have longer gonotrophic cycles. CONCLUSIONS: Focal vector control can reduce vector biting rates in settings where a high larvicidal efficacy can be achieved and an appropriate duration and frequency of larviciding can be ensured. Future work linking SIMPOP with onchocerciasis transmission models will permit evaluation of the impact of combined anti-vectorial and anti-parasitic interventions on accelerating elimination of the disease.