Andes Hantavirus human-to-human transmission dynamics and1 preparedness scenarios
Proverbio, D.; Giordano, G.
Show abstract
Human-to-human transmission of the Andes virus (ANDV), which causes the deadly Hantavirus Pulmonary Syndrome, has been documented in several outbreaks, but remains insufficiently characterized to ensure preparedness against future emergence. Here, we integrate epidemiological data from historical outbreaks and from the 2026 MV Hondius outbreak with a stochastic network-based transmission model to quantify ANDV epidemic potential and control requirements. We estimate a reproduction number of $R_0=2.30$ (95\% CI: 1.33--3.27), supporting sustained human transmission. Uncertainty and sensitivity analysis show that variation in transmissibility and incubation period mainly affects outbreak timing and size, while superspreading events can substantially alter the epidemic evolution. Model analysis and simulations further reveal how contact-network structure strongly modulates outbreak size, with close-contact clustering facilitating containment. Due to the virus characteristics, rapid identification and isolation can effectively suppress outbreaks, even more effectively than in the case of COVID-19, whereas pre-symptomatic transmission or increased mixing could substantially increase risk. Our results provide a quantitative framework for ANDV preparedness and highlight the most critical transmission features for future surveillance.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- Transmission lineage dynamics and the detection of viral importation in emerging epidemics 95%
- Reimagining the serocatalytic model for infectious diseases: a case study of common coronaviruses 95%
- Predicting the impact of COVID-19 non-pharmaceutical intervention on short- and medium-term dynamics of enterovirus D68 in the US 95%
Similar papers in this journal
- Genetic drift acts strongly on within-host influenza virus populations during acute infection but does not act alone 95%
- Within-host infectious disease models accommodating cellular coinfection, with an application to influenza 94%
- Modeling cellular co-infection and reassortment of bluetongue virus in Culicoides midges 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.