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Understanding and mitigating SARS-CoV-2 transmission hinges on antibody and viral RNA data that inform exposure and shedding, but extensive variation in assays, study group demographics and laboratory protocols across published studies confounds inference of true biological patterns. Our meta-analysis leverages 3214 datapoints from 516 individuals in 21 studies to reveal that seroconversion of both IgG and IgM occurs around 12 days post-symptom onset (range 1–40), with extensive individual variation that is not significantly associated with disease severity. IgG and IgM detection probabilities increase from roughly 10% at symptom onset to 98–100% by day 22, after which IgM wanes while IgG remains reliably detectable. RNA detection probability decreases from roughly 90% to zero by day 30, and is highest in feces and lower respiratory tract samples. Our findings provide a coherent evidence base for interpreting clinical diagnostics, and for the mathematical models and serological surveys that underpin public health policies.

Diseases of humans and wildlife are typically tracked and studied through incidence, the number of new infections per time unit. Estimating incidence is not without difficulties, as asymptomatic infections, low sampling intervals and low sample sizes can introduce large estimation errors. After infection, biomarkers such as antibodies or pathogens often change predictably over time, and this temporal pattern can contain information about the time since infection that could improve incidence estimation. Antibody level and avidity have been used to estimate time since infection and to recreate incidence, but the errors on these estimates using currently existing methods are generally large. Using a semi-parametric model in a Bayesian framework, we introduce a method that allows the use of multiple sources of information (such as antibody level, pathogen presence in different organs, individual age, season) for estimating individual time since infection. When sufficient background data are available, this method can greatly improve incidence estimation, which we show using arenavirus infection in multimammate mice as a test case. The method performs well, especially compared to the situation in which seroconversion events between sampling sessions are the main data source. The possibility to implement several sources of information allows the use of data that are in many cases already available, which means that existing incidence data can be improved without the need for additional sampling efforts or laboratory assays.

Many emerging infections are RNA virus spillovers from animal reservoirs. Reservoir identification is necessary for predicting the geographic extent of infection risk, but rarely are taxonomic levels below the animal species considered as reservoir, and only key circumstances in nature and methodology allow intrinsic virus-host associations to be distinguished from simple geographic (co-)isolation. We sampled and genetically characterized in detail a contact zone of two subtaxa of the rodent Mastomys natalensis in Tanzania. We find two distinct arenaviruses, Gairo and Morogoro virus, each spatially confined to a single M. natalensis subtaxon, only co-occurring at the contact zone's centre. Inter-subtaxon hybridization at this centre and a continuum of quality habitat for M. natalensis show that both viruses have the ecological opportunity to spread into the other substaxon's range, but do not, strongly suggesting host-intrinsic barriers. Such barriers could explain why human cases of another M. natalensis-borne arenavirus, Lassa virus, are limited to West Africa.

The trend of the number of publications on a research field is often used to quantify research interest and effort, but this measure is biased by general publication record inflation. This study introduces a novel metric as an unbiased and quantitative tool for trend analysis and bibliometrics. The metric was used to reanalyze reported publication trends and perform in-depth trend analyses on patent groups and a broad range of field in the life-sciences. The analyses confirmed that inflation bias frequently results in the incorrect identification of field-specific increased growth. It was shown that the metric enables a more detailed, quantitative and robust trend analysis of peer reviewed publications and patents. Some examples of the metric’s uses are quantifying inflation-corrected growth in research regarding microplastics (51% ± 10%) between 2012 and 2018 and detecting inflation-corrected growth increase for transcriptomics and metabolomics compared to genomics and proteomics (Tukey post hoc p<0.0001). The developed trend-analysis tool removes inflation bias from bibliometric trend analyses. The metric improves evidence-driven decision-making regarding research effort investment and funding allocation.

Serology (the detection of antibodies formed by the host against an infecting pathogen) is frequently used to assess current infections and past exposure to specific pathogens. However, the presence of cross-reactivity among host antibodies in serological data makes it challenging to interpret the patterns and draw reliable conclusions about the infecting pathogen or strain. In our study, we use microscopic agglutination test (MAT) serological data from three host species [California sea lion (Zalophus californianus), island fox (Urocyon littoralis), and island spotted skunk (Spilogale gracilis)] with confirmed infections to assess differences in cross-reactivity by host species and diagnostic laboratory. All host species are known to be infected with the same serovar of Leptospira interrogans. We find that absolute and relative antibody titer magnitudes vary systematically across host species and diagnostic laboratories. Despite being infected by the same Leptospira serovar, three host species exhibit different cross-reactivity profiles to a 5-serovar diagnostic panel. We also observe that the cross-reactive antibody titer against a non-infecting serovar can remain detectable after the antibody titer against the infecting serovar declines below detectable levels. Cross-reactivity in serological data makes interpretation difficult and can lead to common pitfalls. Our results show that the highest antibody titer is not a reliable indicator of infecting serovar and highlight an intriguing role of host species in shaping reactivity patterns. On the other side, seronegativity against a given serovar does not rule out that serovar as the cause of infection. We show that titer magnitudes can be influenced by both host species and diagnostic laboratory, indicating that efforts to interpret absolute titers (e.g., as indicators of recent infection) must be calibrated to the system under study. Thus, we implore scientists and health officials using serological data for surveillance to interpret the data with caution.

Leptospirosis, the most widespread zoonotic disease in the world, is broadly understudied in multi-host wildlife systems. Knowledge gaps regarding Leptospira circulation in wildlife, particularly in densely populated areas, contribute to frequent misdiagnoses in humans and domestic animals. We assessed Leptospira prevalence levels and risk factors in five target wildlife species across the greater Los Angeles region: striped skunks ( Mephitis mephitis ), raccoons ( Procyon lotor ), coyotes ( Canis latrans ), Virginia opossums ( Didelphis virginiana ), and fox squirrels ( Sciurus niger ). We sampled more than 960 individual animals, including over 700 from target species in the greater Los Angeles region, and an additional 266 sampled opportunistically from other California regions and species. In the five target species seroprevalences ranged from 5 to 60%, and infection prevalences ranged from 0.8 to 15.2% in all except fox squirrels (0%). Leptospira phylogenomics and patterns of serologic reactivity suggest that mainland terrestrial wildlife, particularly mesocarnivores, could be the source of repeated observed introductions of Leptospira into local marine and island ecosystems. Overall, we found evidence of widespread Leptospira exposure in wildlife across Los Angeles and surrounding regions. This indicates exposure risk for humans and domestic animals and highlights that this pathogen can circulate endemically in many wildlife species even in densely populated urban areas.

Studies of infectious disease ecology would benefit greatly from knowing when individuals were infected, but estimating this time of infection can be challenging, especially in wildlife. Time of infection can be estimated from various types of data, with antibody‐level data being one of the most promising sources of information. The use of antibody levels to back‐calculate infection time requires the development of a host‐pathogen system‐specific model of antibody dynamics, and a leading challenge in such quantitative serology approaches is how to model antibody dynamics in the absence of experimental infection data. We present a way to model antibody dynamics in a Bayesian framework that facilitates the incorporation of all available information about potential infection times and apply the model to estimate infection times of Channel Island foxes infected with Leptospira interrogans . Using simulated data, we show that the approach works well across a broad range of parameter settings and can lead to major improvements in infection time estimates that depend on system characteristics such as antibody decay rate and variation in peak antibody levels after exposure. When applied to field data we saw reductions up to 83% in the window of possible infection times. The method substantially simplifies the challenge of modelling antibody dynamics in the absence of individuals with known infection times, opens up new opportunities in wildlife disease ecology and can even be applied to cross‐sectional data once the model is trained.

Density-dependent shifts in population processes like territoriality, reproduction, dispersal, and parasite transmission are driven by changes in contacts between individuals. Despite this, surprisingly little is known about how contacts change with density, and thus the mechanisms driving density-dependent processes. A simple linear contact–density function is often assumed, but this is not based on a sound basis of empirical data. We addressed this question using a replicated, semi-natural experiment in which we measured contacts at feeding stations between multimammate mice, Mastomys natalensis, across ten distinct, linearly increasing densities between 10 and 272 animals/ha. Unexpectedly, unique contacts increased not linearly but sigmoidally with density, which we attribute to the species’ scramble competition mating system, small-scale dominance/avoidance and absence of territoriality. These results provide new insights into how species’ characteristics can relate to density-dependent changes in contacts, and the unexpected shape of the contact–density function warrants that density-dependence in ecological models, such as parasite transmission models, must be parameterized with care.

Rodents serve as reservoirs and/or vectors for several human infections of high morbidity and mortality in the tropics. Population growth and demographic shifts over the years have increased contact with these mammals, thereby increasing opportunities for disease transmission. In Africa, the burden of rodent-borne diseases is not well described. To investigate human seroprevalence of selected rodent-borne pathogens, sera from 657 healthy adults in ten rural communities in Ghana were analyzed. An in-house enzyme-linked immunosorbent assay (ELISA), for immunoglobulin G (IgG) antibodies to Lassa virus was positive in 34 (5%) of the human samples. Using commercial kits, antibodies to hantavirus serotypes, Puumala and Dobrava, and Leptospira bacteria were detected in 11%, 12% and 21% of the human samples, respectively. Forty percent of residents in rural farming communities in Ghana have measurable antibodies to at least one of the rodent-borne pathogens tested, including antibodies to viral hemorrhagic fever viruses. The high seroprevalence found in rural Ghana to rodent-borne pathogens associated with both sporadic cases and larger disease outbreaks will help define disease threats and inform public health policy to reduce disease burden in underserved populations and deter larger outbreaks.

The multimammate mice ( Mastomys natalensis ) is the most-studied rodent species in sub-Saharan Africa, where it is an important pest species in agriculture and carrier of zoonotic diseases (e.g. Lassa virus). Here, we provide a unique dataset that consists of twenty-nine years of continuous monthly capture-mark-recapture entries on one 3 ha mosaic field (MOSA) in Morogoro, Tanzania. It is one of the most accurate and long-running capture-recapture time series on a small mammal species worldwide and unique to Africa. The database can be used by ecologists to test hypotheses on the population dynamics of small mammals (e.g. to test the effect of climate change), or to validate new algorithms on real long-term field data (e.g. new survival analyses techniques). It is also useful for both scientists and decision-makers who want to optimize rodent control strategies and predict outbreaks of multimammate mice.