Modeling the Immune Response for Pathogenic and Nonpathogenic Orthohantavirus Infections in Human Lung Microvasculature Endothelial Cells.

Hantaviruses, genus Orthohantavirus, family Hantaviridae, order Bunyavirales, are negative-sense, single-stranded, tri-segmented RNA viruses that persistently infect rodents, shrews, and moles. Of these, only certain virus species harbored by rodents are pathogenic to humans. Infection begins with inhalation of virus particles into the lung and trafficking to the lung microvascular endothelial cells (LMVEC). The reason why certain rodent-borne hantavirus species are pathogenic has long been hypothesized to be related to their ability to downregulate and dysregulate the immune response as well as increase vascular permeability of infected endothelial cells. We set out to study the temporal dynamics of host immune response modulation in primary human LMVECs following infection by Prospect Hill (nonpathogenic), Andes (pathogenic), and Hantaan (pathogenic) viruses. We measured the level of RNA transcripts for genes representing antiviral, proinflammatory, anti-inflammatory, and metabolic pathways from 12 to 72 h with time points every 12 h. Gene expression analysis in conjunction with mathematical modeling revealed a similar profile for all three viruses in terms of upregulated genes that partake in interferon signaling (TLR3, IRF7, IFNB1), host immune cell recruitment (CXCL10, CXCL11, and CCL5), and host immune response modulation (IDO1). We examined secreted protein levels of IFN-ß, CXCL10, CXCL11, CCL5, and IDO in two male and two female primary HLMVEC donors at 48 and 60 h post infection. All three viruses induced similar levels of CCL5, CXCL10, and CXCL11 within a particular donor, and the levels were similar in three of the four donors. All three viruses induced different protein secretion levels for both IFN-ß and IDO and secretion levels differed between donors. In conclusion, we show that there was no difference in the transcriptional profiles of key genes in primary HLMVECs following infection by pathogenic and nonpathogenic hantaviruses, with protein secretion levels being more donor-specific than virus-specific.

Application of mathematical modelling to inform national malaria intervention planning in Nigeria.

BACKGROUND: For their 2021-2025 National Malaria Strategic Plan (NMSP), Nigeria's National Malaria Elimination Programme (NMEP), in partnership with the World Health Organization (WHO), developed a targeted approach to intervention deployment at the local government area (LGA) level as part of the High Burden to High Impact response. Mathematical models of malaria transmission were used to predict the impact of proposed intervention strategies on malaria burden. METHODS: An agent-based model of Plasmodium falciparum transmission was used to simulate malaria morbidity and mortality in Nigeria's 774 LGAs under four possible intervention strategies from 2020 to 2030. The scenarios represented the previously implemented plan (business-as-usual), the NMSP at an 80% or higher coverage level and two prioritized plans according to the resources available to Nigeria. LGAs were clustered into 22 epidemiological archetypes using monthly rainfall, temperature suitability index, vector abundance, pre-2010 parasite prevalence, and pre-2010 vector control coverage. Routine incidence data were used to parameterize seasonality in each archetype. Each LGA's baseline malaria transmission intensity was calibrated to parasite prevalence in children under the age of five years measured in the 2010 Malaria Indicator Survey (MIS). Intervention coverage in the 2010-2019 period was obtained from the Demographic and Health Survey, MIS, the NMEP, and post-campaign surveys. RESULTS: Pursuing a business-as-usual strategy was projected to result in a 5% and 9% increase in malaria incidence in 2025 and 2030 compared with 2020, while deaths were projected to remain unchanged by 2030. The greatest intervention impact was associated with the NMSP scenario with 80% or greater coverage of standard interventions coupled with intermittent preventive treatment in infants and extension of seasonal malaria chemoprevention (SMC) to 404 LGAs, compared to 80 LGAs in 2019. The budget-prioritized scenario with SMC expansion to 310 LGAs, high bed net coverage with new formulations, and increase in effective case management rate at the same pace as historical levels was adopted as an adequate alternative for the resources available. CONCLUSIONS: Dynamical models can be applied for relative assessment of the impact of intervention scenarios but improved subnational data collection systems are required to allow increased confidence in predictions at sub-national level.

Probability of a zoonotic spillover with seasonal variation.

Zoonotic infectious diseases are spread from animals to humans. It is estimated that over 60% of human infectious diseases are zoonotic and 75% of them are emerging zoonoses. The majority of emerging zoonotic infectious diseases are caused by viruses including avian influenza, rabies, Ebola, coronaviruses and hantaviruses. Spillover of infection from animals to humans depends on a complex transmission pathway, which is influenced by epidemiological and environmental processes. In this investigation, the focus is on direct transmission between animals and humans and the effects of seasonal variations on the transmission and recovery rates. Fluctuations in transmission and recovery, besides being influenced by physiological processes and behaviors of pathogen and host, are driven by seasonal variations in temperature, humidity or rainfall. A new time-nonhomogeneous stochastic process is formulated for infectious disease spread from animals to humans when transmission and recovery rates are time-periodic. A branching process approximation is applied near the disease-free state to predict the probability of the first spillover event from animals to humans. This probability is a periodic function of the time when infection is introduced into the animal population. It is shown that the highest risk of a spillover depends on a combination of animal to human transmission, animal to animal transmission and animal recovery. The results are applied to a stochastic model for avian influenza with spillover from domestic poultry to humans.

Stochastic two-group models with transmission dependent on host infectivity or susceptibility.

Stochastic epidemic models with two groups are formulated and applied to emerging and re-emerging infectious diseases. In recent emerging diseases, disease spread has been attributed to superspreaders, highly infectious individuals that infect a large number of susceptible individuals. In some re-emerging infectious diseases, disease spread is attributed to waning immunity in susceptible hosts. We apply a continuous-time Markov chain (CTMC) model to study disease emergence or re-emergence from different groups, where the transmission rates depend on either the infectious host or the susceptible host. Multitype branching processes approximate the dynamics of the CTMC model near the disease-free equilibrium and are used to estimate the probability of a minor or a major epidemic. It is shown that the probability of a major epidemic is greater if initiated by an individual from the superspreader group or by an individual from the highly susceptible group. The models are applied to Severe Acute Respiratory Syndrome and measles.