iiinova — where your digital self keeps evolving

David Simons @davidsimons.bsky.social · 29/07/2026

Beauty of a postdoc. Nothing better to do than get through revisions.

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David Simons @davidsimons.bsky.social · 29/07/2026

Great work. 2 years under review though, why? Surely not needed.

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David Simons @davidsimons.bsky.social · 07/07/2026

My masters project was on the rodent trapping dataset that was created when this paving was happening. Similar questions, does the rodent community get restructured by altering the local environment. Unfortunately, I think these data are just languishing in the PIs file drawers.

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David Simons @davidsimons.bsky.social · 06/07/2026

Pending vaccine efficacy trials are going to require massive logistical scaling and truly dynamic, ecologically grounded site selection. #OneHealth #LassaFever #Surveillance #EcoEpidemiology #Vaccines

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David Simons @davidsimons.bsky.social · 06/07/2026

This highlights some fundamental challenges of zoonotic disease surveillance. For Lassa there seems to be an enormous asymptomatic component and a highly heterogeneous, patchily distributed hazard of spillover into humans driven by underlying reservoir and pathogen ecology.

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David Simons @davidsimons.bsky.social · 06/07/2026

Excited to see the Enable 1.0 www.thelancet.com/journals/lan... study on #LassaFever published in @lancetgh.bsky.social. A massive effort enrolling 20,000+ participants across 4 countries. Yet, despite 2.5 year follow-up, just 39 symptomatic cases detected.

thelancet.com

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David Simons @davidsimons.bsky.social · 24/06/2026

We should validate this with serosurveys stratified across the built-up gradient in West African cities. Crucially, if we want to power Phase III Lassa vaccine trials, we need to target peri-urban 'silent districts'. Locations where transmission may be ubiquitous but clinical reporting is absent.

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David Simons @davidsimons.bsky.social · 24/06/2026

The ecological outputs highlight complex biotic constraints. The interactions between M. natalensis and invasive synanthropes in urban and peri-urban settings creates an exclusion effect. These multi-species dynamics dictate spatial risk and require high-resolution ecological follow-up.

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David Simons @davidsimons.bsky.social · 24/06/2026

The scale of the burden is substantial, accounting for 'shielding' and antibody waning yields an estimated 2.6m annual LASV infections. Given this does not match reported clinical cases or mortality, the epidemiological missing is huge. A vast proportion of these infections must be paucisymptomatic.

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David Simons @davidsimons.bsky.social · 24/06/2026

To reproduce this, I built an Integrated Multi-Species Occupancy Model for the host community, using rodent surveillance to map the viral hazard. Calibrating this against human seroprevalence allowed me to estimate annual infection whilst modelling the protective 'shield' of the built environment.

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David Simons @davidsimons.bsky.social · 24/06/2026

Really excited to see this peer-reviewed and published and published in epidemiology and infection. This work was motivated by the observation that we do not see #LassaFever outbreaks in sense West African cities despite high predicted biological hazard. doi.org/10.1017/S095...

doi.org

The Socio-economic Shield Limits Lassa Virus Spillover in Urban West Africa | Epidemiology & Infection | Cambridge Core

The Socio-economic Shield Limits Lassa Virus Spillover in Urban West Africa

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Colin Carlson @colincarlson.bsky.social · 22/06/2026

Our lab website is updated with a new postdoc opening! This is a 2-year, in-person position looking at the relationships between climate change, pandemic risk, and policy choices over the 21st century. Please reach out (not on here)! www.carlsonlab.bio/join

carlsonlab.bio

Opportunities — The Carlson Lab

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London School of Hygiene & Tropical Medicine @lshtm.bsky.social · 16/06/2026

📝 LSHTM & @ukhsa.bsky.social have renewed & expanded #UKPHRST academic partnership. 🤝LSHTM will lead a consortium from UK & 6 countries across Africa & SE Asia to co-produce research to protect global health security. Funding: DHSC with UK ODA & NIHR 👉 www.lshtm.ac.uk/newsevents/n...

lshtm.ac.uk

LSHTM to lead research partnership in £18m outbreak response initiative | LSHTM

The London School of Hygiene & Tropical Medicine (LSHTM) has been selected to lead the research arm of the UK Public Health Rapid Support Team (UK-PHRST) in an £18m initiative to continue to help

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David Simons @davidsimons.bsky.social · 16/06/2026

Delighted to support @ricardoriveroh.bsky.social and team on this fantastic preprint. By integrating the macroecological findings from ArHa, he demonstrates that spatial overlap alone is insufficient for effective reassortment. Lineage-specific molecular permissiveness acts as the ultimate filter.

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Steph N. Seifert, PhD @stephseifertphd.bsky.social · 06/06/2026

We're very happy that our study found a home in the EcoHealth journal and we hope the disease ecology community, in particular, find applications of our model!

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David Simons @davidsimons.bsky.social · 04/06/2026

Huge thanks to our co-authors Grant Rickard, Harry Gordon, Ana Martinez Checa, and Dave Redding. Special thanks to the field workers and research teams who collected the original data over decades. None of these global analyses happen without that foundational effort.

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David Simons @davidsimons.bsky.social · 04/06/2026

This started as a "quick" 1 year project as part of my @viralemergence.org Fellowship-in-Residence in 2023 mentored by @stephseifertphd.bsky.social. Massively grateful @ricardoriveroh.bsky.social joined when his PhD started - they're a phenomenal team to work with.

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David Simons @davidsimons.bsky.social · 04/06/2026

7/7 The punchline: pristine, hyper-diverse tropical forests actually exhibit the lowest mean intrinsic hazard. Anthropogenic disturbance acts as an ecological filter, stripping away slow-lived specialists and leaving a high-risk assembly of fast-lived, zoonotic amplifiers.

A global bivariate choropleth map illustrating the intersection of small mammal species richness and mean intrinsic zoonotic hazard (community competence). A three-by-three colour matrix serves as the legend. Teal regions, such as the Amazon basin and Southeast Asia, indicate high species richness but low mean hazard. Pink regions, including northern latitudes and arid zones like Australia, indicate low species richness but high mean hazard. Dark purple regions, such as West and East Africa, Central America, and parts of South America, represent overlapping hotspots of both high biodiversity and high intrinsic hazard. Areas with fewer than two modelled species, including Antarctica and Greenland, are masked in dark grey.

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David Simons @davidsimons.bsky.social · 04/06/2026

6/7 Are these host-virus links ancient? Evolutionary models show broad co-divergence but frequent host-switching. Phylogenetic accumulation curves reveal these novel hosts are almost entirely discovered in reactive bursts immediately following major human outbreaks.

Four panels detailing host phylogenetic diversity (PD) discovery over time. Panel A is a scatter plot of annual change in host PD (ΔPD_t) for arenaviruses. Each point represents a host-year discovery event, coloured by calendar year, with outlined circles indicating the yearly maximum. Panel B shows the equivalent annual change plot for hantaviruses. Spearman trend statistics for yearly maxima and all points are provided in both panels. Panel C displays a line graph of cumulative host PD for arenaviruses based on empirical discovery order. A coloured line tracks the observed trajectory against a red logarithmic least-squares fit and a hatched band representing the 95% null envelope from 10,000 random permutations. Panel D mirrors Panel C for hantaviruses. Light grey vertical bands in Panels C and D mark successive discovery-year intervals.

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David Simons @davidsimons.bsky.social · 04/06/2026

5/7 A fast pace of life (early maturity, massive litters) and synanthropy independently predict reservoir status. Those obligate commensal rodents thriving in human-modified landscapes are associated with viral infection, likely through tolerating and maintaining these viruses.

Three panels illustrating Bayesian phylogenetic model outputs for reservoir status. Panel A shows posterior distributions for pace of life and sampling effort coefficients. A slow pace of life is negatively associated with reservoir status, whereas increased sampling effort is strongly positively associated. Panel B is a line graph showing the marginal effect of surveillance bias, demonstrating a substantially elevated probability of reservoir detection as the number of sampled individuals increases. Panel C is a line graph showing that the probability of reservoir status strictly decreases as the pace of life transitions from fast to slow.

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David Simons @davidsimons.bsky.social · 04/06/2026

4/7 However, once you mathematically correct for this massive surveillance bias using Bayesian phylogenetic dyadic GLMMs, the noise dissipates. Reservoir competence isn't just random; it emerges as a predictable biological trait.

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David Simons @davidsimons.bsky.social · 04/06/2026

3/7 The scale of the anthropogenic filter was surprising. After compiling records for ~590,000 small mammals, we found 46% of rodent and eulipotyphlan genera remain unsampled. Global sampling correlates strictly with night lights and road access, not ecological relevance.

Three bar charts detailing sampling biases in small mammals. Panel A shows taxonomic sampling coverage for rodents, shrews, and hedgehogs; no single genus has more than 50% of its constituent species sampled. Panel B displays sampling bias by synanthropy state: non-synanthropic (NS), occasionally synanthropic (OS), and totally synanthropic (TS). The majority of species are categorised as unknown, but among known states, sampling effort increases progressively from NS to TS. Panel C shows the number of unique viruses tested per species. The distribution is heavily right-skewed; most are tested for 0 to 1 virus, decaying towards 10, with a small secondary peak at 10 and a long tail indicating a few highly investigated species tested for over 30 viruses.

Three line graphs displaying marginal effect plots for spatial surveillance bias predictors. Panel B shows a positive, increasing trend in sampling effort correlated with higher night-time light intensity. Panel C shows a negative trend, with sampling effort decreasing as remoteness (measured by travel time from major cities) increases. Panel D demonstrates that local host species richness has a flat, negligible effect on sampling effort.

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David Simons @davidsimons.bsky.social · 04/06/2026

2/7 The historical #epidemiological data underpinning zoonotic surveillance is "open" but practically, it's a fragmented nightmare. We synthesised 50 years of literature over the last 3 years to address this, creating a harmonised, FAIR-compliant database of 716k assays. github.com/DidDrog11/ar...

github.com

GitHub - DidDrog11/arenavirus_hantavirus: A repository to consolidate published research on Areanviruses and Hantaviruses in rodents.

A repository to consolidate published research on Areanviruses and Hantaviruses in rodents. - DidDrog11/arenavirus_hantavirus

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David Simons @davidsimons.bsky.social · 04/06/2026

1/7 New #ecology preprint out. We’ve spent half a century searching for Arenavirus and Hantavirus reservoirs, but it turns out global surveillance is essentially just us looking under the nearest streetlight. It is decoupled from actual host biodiversity.🧵 doi.org/10.64898/202...

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David Simons @davidsimons.bsky.social · 04/06/2026

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David Simons @davidsimons.bsky.social · 04/06/2026

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David Simons @davidsimons.bsky.social · 04/06/2026

A global bivariate map illustrating the intersection of small mammal species richness and mean intrinsic zoonotic hazard (community competence). A 3x3 colour matrix serves as the legend. Teal regions, such as the Amazon basin and Southeast Asia, indicate high species richness but low mean hazard. Pink regions, including northern latitudes and arid zones like Australia, indicate low species richness but high mean hazard. Dark purple regions, such as West and East Africa, Central America and parts of South America, represent overlapping hotspots of both high biodiversity and high intrinsic hazard. Areas with fewer than two modelled species, such as Antarctica and Greenland, are masked in dark grey.

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David Simons @davidsimons.bsky.social · 04/06/2026

a) Annual change in host phylogenetic diversity (ΔPD_t) for arenaviruses. Each point is a host-year discovery event, coloured by calendar year; outlined circles indicate the yearly maximum ΔPD_t event. b) Annual ΔPD_t for hantaviruses, with the same point encoding and yearly maximum outlines. Spearman trend statistics for yearly maxima (r_max) and all points (r_all) are shown in a) and b) (one-tailed p-values). c) Cumulative host PD for arenaviruses as hosts are added in empirical discovery order. The hatched band is the 95% null envelope from 10,000 random permutations of host discovery order; the observed trajectory is shown as the coloured line and points, and the red curve is the logarithmic least-squares fit. d) Cumulative host PD for hantaviruses, shown as in c). Light grey vertical bands in c) and d) mark successive discovery-year intervals.

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David Simons @davidsimons.bsky.social · 04/06/2026

Figure a) shows the posterior distributions of the PGLMM for pace of life and sampling effort. A slow pace of life is negatively associated with being a reservoir, while increased sampling effort is associated with a substantially elevated probability of being a reservoir. b) shows the marginal effect of surveillance bias alone with an increasing probability of detection with increasing number of individuals sampled. c) shows that as pace of life moves from "fast" to "slow" the probability of reservoir status decreases.

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David Simons @davidsimons.bsky.social · 04/06/2026

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David Simons @davidsimons.bsky.social · 04/06/2026

Bar chart A) shows taxonomic sampling coverage for rodent, shrew and hedgehogs. No single genera exceeds 50% of species within it sampled. Bar chart B) shows sampling biases by synanthropy states of speecies. NS refers to non-synanthropic species, OS are occasionally synanthropic species and TS are totally synanthropic. The majority of species fall in the unknown category. There is an increasing trend as you move from non-synanthropic through to totally synanthropic. Bar chart C) shows the number of unique viruses tested for by species. Most species are tested for 0-1 species, with a decay towards 10 viruses per species. There is a small secondary peak around 10 unique viruses suggesting some species are heavily investigated with the few species being tested for over 30 virus species.

Marginal effect plots of surveillance bias b) Night time lights, c) Remoteness and d) Host Richness. b) shows an increasing trend of sampling with more nighttime lights. c) shows a trend of decreasing sampling with increased remoteness (i.e., decreasing accessibility time/travel time from cities). d) shows no important effect of host species richness on sampling effort.

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David Simons @davidsimons.bsky.social · 04/06/2026

2/7 The historical #epidemiological data underpinning zoonotic surveillance is "open," but practically, it's a fragmented nightmare. We synthesised 50 years of literature over the last 3 years to address this, creating a harmonised, FAIR-compliant database of 716k assays. github.com/DidDrog11/ar...

github.com

GitHub - DidDrog11/arenavirus_hantavirus: A repository to consolidate published research on Areanviruses and Hantaviruses in rodents.

A repository to consolidate published research on Areanviruses and Hantaviruses in rodents. - DidDrog11/arenavirus_hantavirus

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David Simons @davidsimons.bsky.social · 04/06/2026

A huge thank you to my collaborators: Prof. Heikki Henttonen for access to the invaluable Pallasjärvi long-term archives, and the team at the Grimsö Wildlife Research Station for facilitating the longitudinal field validation.

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David Simons @davidsimons.bsky.social · 04/06/2026

Thankfully I will be conducting this project with the support of @elinvidevall.bsky.social, relying on her molecular microbiology expertise to understand what is happening in the microbiome of voles.

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David Simons @davidsimons.bsky.social · 04/06/2026

I suppose this actually makes me real ecologist now and I'm excited to join the university where Linnaeus helped establish the field of modern ecology.

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David Simons @davidsimons.bsky.social · 04/06/2026

Resolving these overlapping spatial and social interactions could help us model the transmission dynamics of environmentally persistent PUUV and rodent zoonoses more broadly. It will also provide tool to characterise contact networks in settings where tagging rodents is not feasible (Lassafever).

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David Simons @davidsimons.bsky.social · 04/06/2026

Through partitioning microbial similarity into explicit transmission channels, is it possible to understand how functional connectivity between hosts is restructured across shifting demographic and environmental landscapes?

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David Simons @davidsimons.bsky.social · 04/06/2026

Funded by the Sintring Foundation this two year project aims to assess whether we can develop a "Microbial Sensor": Can we use high-resolution gut microbiome sequencing to reconstruct the social and environmental contact networks of wild Fennoscandian rodents?

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David Simons @davidsimons.bsky.social · 04/06/2026

After a couple of failed applications, I am very excited that one panned out. I will be starting my first independently funded fellowship in September! My project will be hosted at the @animecol-uu.bsky.social Uppsala University #DiseaseEcology #Microbiome #SpatialEpidemiology #Hantavirus

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The Carlson Lab @ Yale @carlsonlab.bsky.social · 25/09/2025

🚨 We're about to start reviewing applications, but there's still time to reach out for our postdoc position on climate change impact attribution! If you have experience with attribution science or climate epidemiology, and want to help us launch the Global Burden of Climate Change Study, reach out!

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David Simons @davidsimons.bsky.social · 23/06/2025

Even more upsetting was when they said they'd love to share the data but they'd lost it because it wasn't archived or retained.

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Colin Carlson @colincarlson.bsky.social · 21/06/2025

NEW! 🎉 We need wildlife disease surveillance to predict epidemics, but data sharing is rare - we found that only 2-3% of studies share raw data. So, we spent three years developing a data standard and R package to help get wildlife disease data into FAIR repositories. www.nature.com/articles/s41...

The title of the paper: "A minimum data standard for wildlife disease research and surveillance" - and an example data table

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James Hassell @jameshassell.bsky.social · 26/04/2025

📣 We’re hiring! 2-yr postdoc at Smithsonian’s NZCBI on an NSF-funded project with partners incl. Glasgow, RVC, Uganda MOH, Cary Institute. Focus: land use change, rodent movement, human-rodent contact, modeling disease risk, w/ plenty of fieldwork in Uganda. Details: tinyurl.com/SINSFR

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David Simons @davidsimons.bsky.social · 30/01/2025

Are any #Russian speakers are able to assist with this. Translation is from Google Translate does the original Russian state the species name or are they describing them similarly?

Machine translation from original russian text.

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Colin Carlson @colincarlson.bsky.social · 15/01/2025

🚨😷🧪 NEW: A growing body of evidence shows that pandemics, biodiversity loss, and climate change are part of a broader polycrisis - but there are no simple solutions. A sweeping overview of "Pathogens and planetary change" for the first issue of @natrevbiodiv.bsky.social, out now 🔓 rdcu.be/d6lHl

Top panels: graphs showing increases in spillover events, extinction rates, and temperature anomalies over the last few centuries. Bottom panel: a map of 10 pandemics since the year 1900. Four were linked to agriculture, two to wildlife use, and one to climate change.

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