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

Modeling the Immune Response for Pathogenic and Nonpathogenic <i>Orthohantavirus</i> 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-β, 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-β 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

Additional file 1 of Application of mathematical modelling to inform national malaria intervention planning in Nigeria

Additional file 1: Figure S1. Assignment of 774 LGAs in Nigeria into 22 epidemiological archetypes. Figure S2. Simulated seasonality of clinical malaria by archetype compared with the Rapid Impact Assessment health facility data for years 2014 – 2018. Thin red lines show 50 stochastic realizations and solid red dots and line show the mean over the realizations. Figure S3. a: Case management among children under the age, insecticide treated nets use and PfPR among children under the age of five years in 2010 by LGA. b: Two plots are shown for each archetype with archetype names at the top of each plot. The left plot is the larval habitat multiplierand likelihood evaluation against archetype U5 PfPR 2010 MIS. The red dot is maximum likelihood estimate of LHM. The right plot is the simulated U5 PfPR within each archetype compared with monthly U5 PfPR from the 2010 MIS. The thick red line indicates the best match while thin red lines show PfPR under other larval habitat scale factors. Each line is the mean of 10 stochastic realizations. 12 out of the 22 archetypes are shown here, remainder are shown in Fig S3b. Figure S4. ITN coverage among pregnant women attending ANC in 2018. Figure S5. Estimated ITN kill rate for a 12% reduction in annual malaria incidence among children under the age of five years. Figure S6. The relationship between permethrin bioassay mortality and ITN killing rate in the Churcher et al. model and EMOD. Scale factors was calculated by dividing the EMOD kill rateby the kill rate from the Churcher et al. model. Figure S7. Fitted splines showing estimated IPTp coverage through time for a random subset of LGAs. Points show DHS/MIS data and lines show the fitted splines, with each color indicating a different LGA. Figure S8. Fraction of IPTp-receiving individuals who reported receiving one, two, or three or more doses in each DHSor MIS. Figure S9. LGAs designated as eligible to receive IPTi. Figure S10. Mean DTP1-3 vaccine coverage per LGA in the 2018 DHS survey. Figure S11. Archetype level scatterplot highlighting beta-regression model equations used to compute predicted average yearly change CM coverages per archetype. Annual case management is the proportion of children with fever in the 2 weeks period prior to the survey that received an ACT. Figure S12. Malaria seasonality in routine health facility data and simulation for 37 Nigerian states in 2014. Incidence values in the health facility data were scaled by the median relative difference between the simulation and RIA data by state. Vertical purple horizontal lines are 95% confidence intervals for the RIA data. Vertical blue lines are the ranges of the simulations from 5 seed runs. Figure S13. Comparison of DHIS2 and simulation seasonality trends in 2014 with a cross-correlation function. CCF at the time lag zero is a measure of the contemporaneous correlation or the linear relationship between the two time series. Figure S14. Malaria seasonality in routine health facility data and simulation for 37 Nigerian states in 2015. Incidence values in the health facility data were scaled by the median relative difference between the simulation and RIA data by state. Vertical purple horizontal lines are 95% confidence intervals for the RIA data. Vertical blue lines are the ranges of the simulations from 5 seed runs. Figure S15. Comparison of DHIS2 and simulation seasonality trends in 2015 with a cross-correlation function. CCF at the time lag zero is a measure of the contemporaneous correlation or the linear relationship between the two time series. Figure S16. Malaria seasonality in routine health facility data and simulation for 37 Nigerian states in 2016. Incidence values in the health facility data were scaled by the median relative difference between the simulation and RIA data by state. Vertical purple horizontal lines are 95% confidence intervals for the RIA data. Vertical blue lines are the ranges of the simulations from 5 seed runs. Figure S17. Comparison of DHIS2 and simulation seasonality trends in 2016 with a cross-correlation function. CCF at the time lag zero is a measure of the contemporaneous correlation or the linear relationship between the two time series. Figure S18. Malaria seasonality in routine health facility data and simulation for 37 Nigerian states in 2017. Incidence values in the health facility data were scaled by the median relative difference between the simulation and RIA data by state. Vertical purple horizontal lines are 95% confidence intervals for the RIA data. Vertical blue lines are the ranges of the simulations from 5 seed runs. Figure S19. Comparison of DHIS2 and simulation seasonality trends in 2017 with a cross-correlation function. CCF at the time lag zero is a measure of the contemporaneous correlation or the linear relationship between the two time series. Figure S20. Malaria seasonality in routine health facility data and simulation for 37 Nigerian states in 2018. Incidence values in the health facility data were scaled by the median relative difference between the simulation and RIA data by state. Vertical purple horizontal lines are 95% confidence intervals for the RIA data. Vertical blue lines are the ranges of the simulations from 5 seed runs. Figure S21. Comparison of DHIS2 and simulation seasonality trends in 2018 with a cross-correlation function. CCF at the time lag zero is a measure of the contemporaneous correlation or the linear relationship between the two time series