Epidemics on networks: Reducing disease transmission using health emergency declarations and peer communication

Understanding individual decisions in a world where communications and information move instantly via cell phones and the internet,contributes to the development and implementation of policies aimed at stopping or ameliorating the spread of diseases.In this manuscript,the role of official social network perturbations generated by public health officials to slow down or stop a disease outbreak are studied over distinct classes of static social networks.The dynamics are stochastic in nature with individuals(nodes)being assigned fixed levels of education or wealth.Nodes may change their epidemiological status from susceptible,to infected and to recovered.Most importantly,it is assumed that when the prevalence reaches a pre-determined threshold level,P*,information,called awareness in our framework,starts to spread,a process triggered by public health authorities.Information is assumed to spread over the same static network and whether or not one becomes a temporary informer,is a function of his/her level of education or wealth and epidemiological status.Stochastic simulations show that threshold selection Pand the value of the average basic reproduction number impact the final epidemic size differentially.For the ErdÖos-Rényi and Small-world networks,an optimal choice for Pthat minimize the final epidemic size can be identified under some conditions while for Scalefree networks this is not case.

A risk-based model for predicting the impact of using condoms on the spread of sexually transmitted infections

We create and analyze a mathematical model to understand the impact of condom-use and sexual behavior on the prevalence and spread of Sexually Transmitted Infections(STIs).STIs remain significant public health challenges globally with a high burden of some Sexually Transmitted Diseases(STDs)in both developed and undeveloped countries.Although condom-use is known to reduce the transmission of STIs,there are a few quantitative population-based studies on the protective role of condom-use in reducing the incidence of STIs.The number of concurrent partners is correlated with their risk of being infectious by an STI such as chlamydia,gonorrhea,or syphilis.We develop a Susceptible-Infectious-Susceptible(SIS)model that stratifies the population based on the number of concurrent partners.The model captures the multi-level heterogeneous mixing through a combination of biased(preferential)and random(proportional)mixing processes between individuals with distinct risk levels,and accounts for differences in condom-use in the low-and high-risk populations.We use sensitivity analysis to assess the relative impact of high-risk people using condom as a prophylactic intervention to reduce their chance of being infectious,or infecting others.The model predicts the STI prevalence as a function of the number of partners of an individual,and quantifies how this distribution of effective partners changes as a function of condom-use.Our results show that when the mixing is random,then increasing the condom-use in the high-risk population is more effective in reducing the prevalence than when many of the partners of high-risk people have high risk.The model quantifies how the risk of being infected increases for people who have more partners,and the need for high-risk people to consistently use condoms to reduce their risk of infection.