{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,3]],"date-time":"2026-08-03T15:57:17Z","timestamp":1785772637313,"version":"3.56.0"},"update-to":[{"DOI":"10.12688\/wellcomeopenres.25937.1","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T00:00:00Z","timestamp":1785196800000}}],"reference-count":79,"publisher":"F1000 Research Ltd","license":[{"start":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T00:00:00Z","timestamp":1785196800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000268","name":"Biotechnology and Biological Sciences Research Council","doi-asserted-by":"publisher","award":["BB\/X005364\/1"],"award-info":[{"award-number":["BB\/X005364\/1"]}],"id":[{"id":"10.13039\/501100000268","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100010269","name":"Wellcome Trust","doi-asserted-by":"publisher","award":["220179"],"award-info":[{"award-number":["220179"]}],"id":[{"id":"10.13039\/100010269","id-type":"DOI","asserted-by":"publisher"}]},{"name":"NSF\u2010NIH\u2010NIFA Ecology and Evolution of Infectious Disease Award","award":["2208034"],"award-info":[{"award-number":["2208034"]}]}],"content-domain":{"domain":["wellcomeopenresearch.org"],"crossmark-restriction":false},"short-container-title":["Wellcome Open Res"],"abstract":"<ns5:p>\n                    Introduction Zoonotic spillover driven by human activity remains a critical challenge to human health. Research focusing on disease ecology or human\u2013animal interactions provides detailed local insights, but these findings are often siloed and disconnected from global processes and policy. Broad-scale maps of predicted disease risk are more commonly used to inform decisions, yet their connection to local-scale drivers of pathogen transmission remains poorly understood. This study bridges these gaps through a cross-scale study of Lassa fever, a rodent-borne haemorrhagic fever of public health significance in West Africa and a WHO priority pathogen. We use a fine-scale quantitative and participatory modelling approach that explicitly integrates into broad-scale risk models to identify the patterns and processes that drive spillover within human-driven ecosystems. Lassa fever epidemics are dominated by endemic and seasonal transmission of\n                    <ns5:italic>Mammarenavirus lassaense<\/ns5:italic>\n                    (LASV) from rodent reservoirs to humans within a rural context, positioning LASV as a uniquely tractable system in which to study zoonotic spillover. Methods and Analysis We describe parallel longitudinal observational studies of humans, rodents, and landscapes that are explicitly designed to feed into a cross-scale quantitative modelling framework. By integrating data on landcover, rodent dynamics, human behavior, and LASV infection, we examine how anthropogenic activities shape LASV ecology and human exposure. These data will inform spatial models of the human-rodent-LASV interface to predict key drivers of human exposure risk. Emergent patterns from these models will then be integrated into an existing broad-scale regression-based risk model, extending fine scale mechanism to regional prediction. Together, our empirical research and model predictions will clarify how zoonotic risk propagates from local to regional scales, providing evidence to inform disease management efforts for Lassa fever and zoonotic spillover more broadly.\n                  <\/ns5:p>","DOI":"10.12688\/wellcomeopenres.25937.2","type":"journal-article","created":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T07:29:07Z","timestamp":1785223747000},"page":"125","update-policy":"https:\/\/doi.org\/10.12688\/wellcomeopenres.crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Scaling Lassa Virus Dynamics within Anthropogenic Ecosystems (SCAPES) Study Protocol: a mixed-methods observational cohort study of humans, rodents, and landscapes in Nigeria."],"prefix":"10.12688","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1664-5180","authenticated-orcid":false,"given":"Sagan","family":"Friant","sequence":"first","affiliation":[{"name":"Department of Anthropology 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We are grateful for the clarifications requested, each of which has improved the transparency of the manuscript. We&nbsp;respond to each in turn.  \n<b>1. Longitudinal duration and timing.<\/b> The follow-up period corresponds to the funded SCAPES field period. Longitudinal geo-ethnography and rodent sampling will run over 24 months and replicate serological sampling of the human cohort is a single follow-up event at the conclusion of this observational period, capturing cumulative incident infections. We have stated the 24-month duration explicitly and clarified this temporal structure in the Geo-ethnography section, where it was previously left implicit.  \n<b>2. Serological assay performance.<\/b> We have added the sensitivity, specificity, and accuracy of the Panadea&reg; LASV IgG assay to the Questionnaire and serosurvey section, together with a note on the distinction between dried blood spots (DBS) and whole blood. We anticipate some reduction in sensitivity arising from the DBS elution process, and we now acknowledge this explicitly as a caveat on prevalence estimates. On the analytical handling of misclassification, we have made a deliberate choice not to incorporate assay parameters as informative priors in the Bayesian models. Doing so would add computational complexity and widen uncertainty intervals around prevalence without materially altering inference on the spatial and socio-ecological risk factors that are the primary targets of the study. We felt it was preferable to state this reasoning openly rather than leave the decision unexplained.  \n<b>3. Power for longitudinal incidence.<\/b> This is a helpful prompt to be clearer about scope. The repeat serology within the geo-ethnography cohort (~90 individuals) is not designed or powered to estimate population-level incidence at the village scale. It is an exploratory, within-person component intended to link individual movement footprints and documented behaviours directly to infection outcomes. Because the aim is to characterise socio-ecological exposure profiles rather than to generate generalisable incidence rates, a formal power calculation for population-level seroconversion would not be the appropriate justification. We have added text to the Geo-ethnography section making this scope explicit and stating that the component is not intended to yield incidence estimates generalisable to populations lacking high-resolution behavioural data.  \n<b>4. Model complexity and parsimony.<\/b> We share the reviewer's concern. Alongside non-negative matrix factorisation for dimensionality reduction, our framework relies on regularisation within the Bayesian models \u2014 regularising priors (e.g. horseshoe or ridge penalties on regression coefficients) that shrink weakly informative predictors toward zero \u2014 combined with out-of-sample cross-validation to assess generalisability. Both safeguards are now stated explicitly in Interface modelling and scaling.  \n<b>5. Data sharing and governance.<\/b> Tabular baseline data will be openly available, and we now direct readers to the repository associated with our baseline serosurvey manuscript. Raw geographic coordinates from the geo-ethnography cohort will not be published. Spatial data will instead be de-identified by projecting each village's movement data into a local coordinate frame via an affine transformation: the bounding box is translated to an origin of (0,0) and a random rotation applied, preserving the relative spatial and topological relationships required to reproduce movement and behavioural analyses. Because this transformation is distance-preserving, we describe it as de-identification rather than full anonymisation, and the transformation parameters will be withheld. Analysis code will be openly available via version control, with archived releases deposited to obtain persistent identifiers. These details have been added to the Data availability section.","URL":"https:\/\/wellcomeopenresearch.org\/articles\/11-125\/v2#referee-comment-6662","order":1,"name":"referee-comment-6662","label":"Referee Comment","group":{"name":"article-reports","label":"Article Reports"}},{"value":"10.21956\/wellcomeopenres.29257.r162422, Joel Henrique Ellwanger, Department of Genetics, Universidade Federal do Rio Grande do Sul - UFRGS (Ringgold ID: 124596), Porto Alegre, State of Rio Grande do Sul, 29 Jul 2026, version 2, indexed","URL":"https:\/\/wellcomeopenresearch.org\/articles\/11-125\/v2#referee-response-162422","order":2,"name":"referee-response-162422","label":"Referee Report","group":{"name":"article-reports","label":"Article Reports"}},{"value":"10.21956\/wellcomeopenres.28548.r148273, Gebbiena M. Bron, Norwegian Veterinary Institute, \u00c5s, 23 Apr 2026, version 1, 3 approved with reservations","URL":"https:\/\/wellcomeopenresearch.org\/articles\/11-125\/v1#referee-response-148273","order":3,"name":"referee-response-148273","label":"Referee Report","group":{"name":"article-reports","label":"Article Reports"}},{"value":"<b>Sagan Friant<\/b>; \n<i>Posted: 16 Jul 2026<\/i>; We are grateful to the reviewer for an exceptionally thorough and constructive report. The comments on sample size, rodent trapping detail, environmental transmission, and the conceptual definition of risk have materially improved the protocol, and we appreciate the reviewer's candour in identifying where their own expertise was a limiting factor \u2014 this made it easier for us to see where our description was doing too much work implicitly. We have grouped our responses thematically, following the structure of the review.  \n<b>Introduction and rationale<\/b> We have added biological context on the reservoir. Longitudinal demography of \n<i>M. natalensis<\/i> derives largely from agro-ecological studies in East Africa and Guinea, and we now state an approximate mean wild lifespan of 3 months. On infection dynamics, horizontal transmission typically produces acute infection followed by a period of viral shedding, whereas vertical transmission is hypothesised to result in chronic, lifelong infection. We have been explicit that variation in infectiousness across a host's lifespan remains a key unknown in Lassa ecology and have included laboratory-observed PCR detection windows to give readers a baseline for the system's temporal dynamics. On the mismatch between local observation and national or global intervention, the reviewer has identified the masking effect of administrative aggregation, and we have made this concrete in the Introduction: national surveillance metrics routinely obscure acute spatial and temporal heterogeneity at State and LGA levels, such that a low-risk national forecast may entirely miss a localised surge in a single LGA. This has direct implications for when and where interventions are targeted (such as risk communication or vaccines, if and when they become available), and is the mismatch this project seeks to resolve.&nbsp; We agree that the protocol was insufficiently explicit about what &quot;intervention&quot; means here, and that this ambiguity also undercut our sample size rationale. Our study is designed to identify modifiable pathways within the Lassa DAG, and we now describe interventions at three scales in the Introduction: individual and household (rodent-proof food storage, improved housing construction, household rodent control); community (targeted rodent control campaigns, health literacy); and regional or national (evidence-based behavioural and educational programmes tailored to high-risk socio-ecological motifs). These are preventative rather than reactive, which we now state directly. Relatedly, we have revised the goal statement so that it explicitly links the forecasting tool to intervention design, establishing a structural bridge into Aim 3 that the original phrasing left implicit.  \n<b>Study design, timeline, and sample size<\/b> We accept that the original protocol did not make the sequence of activities legible. We have added a study timeline setting out what happens when, where, and by whom across the baseline, longitudinal, and validation phases, and we have consolidated sample size justification into a dedicated section rather than distributing it across the aims. For the baseline serosurvey, we now present formal power calculations. Using established Lassa seroprevalence tiers (high &gt;40%, medium 10\u201320%, low &lt;5%), a sample of 180 individuals per category provides 80% power (\u03b1 = 0.05) to detect differences between risk levels. We also describe the systematic sampling approach (approaching every \n<i>n<\/i>th household) and the intra-household quota system (adult male, adult female, adolescent, child) designed to prevent demographic sampling bias. For the GPS cohort, we take the reviewer's scepticism seriously and want to be straightforward about the logic. Classical frequentist power calculations sit awkwardly with the spatial autocorrelation inherent in tracking data, and we do not claim otherwise. The cohort (30 households per village, \u224890 individuals) is instead parameterised to achieve thematic and occupational saturation. In rural subsistence settings, spatial behaviour is constrained by a highly conserved set of livelihoods \u2014 cultivation, transit, peridomestic processing \u2014 and tracking 30 households captures the dominant motifs of community space use while sitting at the logistical ceiling for intensive daily device recovery and community sensitisation. We have stated this reasoning, and its limits, explicitly rather than implying a power basis that does not exist. The reviewer's concern about within- versus between-site variation is well placed, and our answer rests on site selection rather than sample size alone. Candidate villages were identified from OpenStreetMap using a bespoke site-selection tool and stratified across a gradient of postulated spillover drivers, with land cover (ESA WorldCover) characterised within a 2 km radius. We targeted medium-sized villages (100\u2013500 households) and excluded sites with &gt;60% forest cover or &gt;20% built-up area. Final selection was checked to ensure no systematic bias relative to other regional settlements, and to confirm that sampled villages spanned local variation in cropland and grassland proportions. The design therefore samples a genuine spectrum of spillover risk across high-, medium-, and low-risk tiers rather than opportunistically targeting known hotspots \u2014 which is what makes between-site extrapolation defensible. We have clarified this process in the Study site and population and Interface modelling and scaling sections. Finally, the reviewer raises the possibility that dimensionality reduction may simply recover the &quot;known&quot; high-level risk map parameters. This is a real risk that we have designed against. We apply Bayesian regularisation via shrinkage priors, so that a fine-scale, drone-derived motif is retained only if it contributes statistically independent predictive skill over the broad-scale baseline. This is now described in Data integration and dimensionality reduction.  \n<b>Rodent ecology and trapping<\/b> We have expanded the rodent methods considerably and specified the trapping protocol in full. In the agricultural\/forest matrix, each village receives six 10 &times; 10 grids deployed at half density (50 traps per grid at alternating stations, ~13 m spacing) to maximise spatial coverage while limiting inter-trap interference. In the peridomestic environment, approximately 210 traps per village are deployed: six traps inside each of the ~30 enrolled geo-ethnography households (180) plus 30 outdoor traps across shared village compounds. Both Sherman and Tomahawk live traps are used to capture the range of small-mammal body sizes. Trapping runs for four consecutive nights per session, quarterly, giving 2,040 trap-nights per village per session and a cumulative effort of 48,960 trap-nights over the 24-month study. Captured animals are euthanised \u2014 the reviewer correctly inferred that this was an omission rather than an ambiguity \u2014 as lethal sampling is required to collect spleen and liver for high-sensitivity RT-PCR. Trap density and peridomestic allocation were calibrated during piloting to match the processing capacity of our mobile field laboratories. On the relationship between trap results and density, we agree that absolute density (individuals per hectare) is not estimable without closed-population capture\u2013mark\u2013recapture. We therefore use hierarchical N-mixture models (spAbundance) to estimate latent relative abundance, modelling species- and site-specific detection probabilities explicitly from the regularised grid design. We now describe this as relative abundance throughout, rather than implying density. On temporal dynamics, quarterly deployments are designed to capture seasonal reproductive pulses rather than cohort survival, and we now say so. We supplement this with age structure across cross-sectional sampling events, allowing identification of juvenile recruitment surges following harvest. We also note that, given the \n<i>r<\/i>-selected reproductive capacity of \n<i>M. natalensis<\/i> and rapid compensatory immigration from the surrounding agricultural matrix, temporary lethal off-take is not expected to meaningfully perturb local population equilibria; we have added the relevant citation and flagged this assumption explicitly rather than leaving removal effects unaddressed in the model. The camera trap suggestion is a good one and we have adopted it in modified form: exploratory camera deployments at a subset of trapping stations will allow us to compare the number of rodents interacting with traps against those actually captured, giving a handle on seasonal trap-shyness and on the food-availability bias the reviewer identifies. We are grateful for the suggestion. On whether full community composition is necessary: we think it is, and the reason is inferential rather than descriptive. Synanthropic competitors \u2014 particularly \n<i>Rattus rattus<\/i> and \n<i>Mus musculus<\/i> \u2014 exert strong structural influence on \n<i>Mastomys<\/i> peridomestic occupancy. An absence of \n<i>Mastomys<\/i> inside a household is uninformative unless we can establish whether the domestic niche is genuinely vacant or is competitively saturated by non-competent hosts. The detection of LASV by PCR in sympatric rodent and shrew species reinforces the case for broader sampling. We have added this justification to Rodent sampling and diagnostics.  \n<b>Viral dynamics and the infectious environment<\/b> The interface model as specified is a hierarchical spatial model of exposure risk, not a mechanistic compartmental (SIR-type) transmission model, and it does not contain an explicit infectious-environment compartment. We considered parameterising one. Indirect transmission via contaminated grain stores, mounded crops, peridomestic surfaces, and aerosolised dust is a documented exposure pathway, but the environmental survival limits of LASV virions across heterogeneous rural surfaces \u2014 as a function of surface type, temperature, humidity, and UV \u2014 remain poorly characterised, and we do not measure environmental persistence directly. An explicit decay compartment would therefore rest on substantial parameter speculation, and we judged that a poor trade against the appearance of mechanistic rigour it would confer. Our approach instead treats environmental contamination as an unobserved latent effect: spatial random fields within the hierarchical models absorb unmeasured, site-specific environmental hazard variance without over-parameterising biology we have not measured. We have added a paragraph to the final section of the Discussion stating plainly that the absence of environmental viral dynamics is a limitation on interpretation, identifying it as a priority for future work, and noting that the longitudinal rodent infection data generated here would support a more explicitly mechanistic transmission model should the environmental persistence parameters become available.  \n<b>Aim 2: scaling, validation, and section organisation<\/b> We recognise the ambiguity around upscaling and have revised Interface modelling and scaling to provide more details. Prediction will not be projected onto administrative boundaries or irregular patches, but onto a regular 1 km&sup2; raster grid. This aligns with standard macro-ecological covariate layers (ESA WorldCover, CHELSA) and avoids the spatial distortion associated with the Modifiable Areal Unit Problem, while remaining fine enough to support operational risk stratification. On the reviewer's suggestion to relocate survey detail: We take this to refer to the participatory methods, which were described in greater procedural detail under the Aim 2 validation surveys than at their first mention. We agree this meant the reader encountered the framework before having the context to interpret it. We have therefore moved the descriptive detail up to the participatory rural appraisal section where these methods are first introduced, so that the approach is intelligible at first pass without requiring the reader to look ahead \u2014 or to consult the associated methods paper. The validation section now describes only what is distinctive about the streamlined &quot;light-touch&quot; adaptation.  \n<b>Data governance and FAIR implementation<\/b> Analytical pipelines, spatial covariates, and scripts will be version-controlled under the RISK Lab GitHub organisation, with partner repositories forked centrally at the point of manuscript submission to ensure link stability; analytical milestones and published codebases will be archived to generate version-specific DOIs; and SOPs for field and laboratory work will be hosted in the repository's open documentation. Given the sensitivity of high-resolution GPS tracks, raw coordinates will not be published: movement data will be de-identified by affine transformation into a local coordinate frame (bounding box translated to origin, random rotation applied), preserving the relative spatial and topological structure needed to reproduce the analyses. Because this transformation is distance-preserving, we describe it as de-identification rather than anonymisation and withhold the transformation parameters. These details are now in the Data availability section.  \n<b>Terminology: risk, hazard, and exposure<\/b> The reviewer is right that our use of &quot;risk&quot; was doing too much work. We now define the primary endpoint as the probability of pathogen \n<i>exposure<\/i>, indexed serologically by anti-LASV IgG, and distinguish it explicitly from the probability of clinical \n<i>disease<\/i>. Our own baseline data make the reviewer's point for us: among enumerated individuals, none reported a historical clinical diagnosis of Lassa fever. We have added a sentence to Interface modelling and scaling clarifying that infection is not a case. We have also adopted the reviewer's framing directly, and it improved the manuscript. Risk is now explicitly conceptualised as the joint product of environmental Hazard (the spatiotemporal abundance of infected rodents and environmental viral load) and Exposure (human behaviour, movement, and time spent in contact with that hazard). This framing is stated in Interface modelling and scaling and now carries through the Discussion.  \n<b>Tense, spelling, and accessibility<\/b> We have harmonised tense to match the current state of the study, as the reviewer suggests: completed work (the baseline assessment) in past tense, ongoing work in present, and planned work in future. We have also standardised spelling conventions throughout. Following the reviewer's specific suggestions, we have revised the abstract to be intelligible to readers outside disease ecology \u2014 referring to a critical challenge to human health, to pathogen exposure and disease burden, and to local drivers of pathogen transmission, in place of terminology that presumed a disease-ecology frame.  \n<b>Discussion and figures<\/b> The principal gain of this work is the reduction of endemic rodent-borne disease burden and the bridging of local and national intervention design; pandemic preparedness is a secondary implication and framing it around COVID-19 in particular was a distraction that we have removed. On the unintended consequences of interventions, the reviewer raises something we regard as a genuine research imperative: uncoordinated rodent culling can trigger compensatory breeding and accelerated juvenile immigration, with the potential to increase rather than reduce risk. Formally parameterising these feedback loops sits beyond this data-collection protocol, but investigating such systemic backfires through scenario-based simulation is a core objective of the Aim 3 co-design framework, and we have said so explicitly, together with a note on how co-designed interventions could be structured so as to be evaluable. Figure 2 has been redesigned as the fully articulated 60-node DAG was indeed illegible in print. We have included a simplified modular causal diagram organised across three tiers \u2014 upstream drivers, the socio-ecological interface, and clinical trajectories \u2014 which preserves directional structure and the overarching One Health logic for clarity for print readers, while the complete network is retained as a figure panel for online readers and on the project website, where readers can zoom and interrogate individual edges. On Figure 4, we should clarify what the panel shows. Panel A depicts the predicted \n<i>probability of occurrence<\/i> of Lassa fever, derived from broad-scale models of reported clinical cases, rather than a continuous incidence rate. Translating these 0\u20131 probabilities into average cases per 100,000 per year is not possible without additional modelling. A separate descriptive map of raw reported LGA case rates would be feasible, but would principally capture passive surveillance ascertainment rather than the underlying socio-ecological transmission hazard our sites were selected to investigate \u2014 which is the very conflation the reviewer elsewhere asks us to avoid. We have therefore clarified the figure legend and the accompanying text rather than adding the layer.","URL":"https:\/\/wellcomeopenresearch.org\/articles\/11-125\/v2#referee-comment-6664","order":4,"name":"referee-comment-6664","label":"Referee Comment","group":{"name":"article-reports","label":"Article Reports"}},{"value":"This work was supported by  Wellcome Trust grant 220179\/A\/20\/Z, the joint NSF\u2010NIH\u2010NIFA Ecology and Evolution of Infectious Disease Award #2208034, UK Research and Innovation (UKRI) Biotechnology and Biological Sciences Research Council (BBSRC) Award BB\/X005364\/1. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.","order":5,"name":"grant-information","label":"Grant Information"},{"value":"This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.","order":0,"name":"copyright-info","label":"Copyright"}]}}