Improving Estimates of Social Contact Patterns for Airborne Transmission of Respiratory Pathogens

Beyond schistosomiasis: unraveling co-infections and altered immunity

Schistosomiasis is a neglected tropical disease caused by the helminth Schistosoma spp. and has the second highest global impact of all parasites. Schistosoma are transmitted through contact with contaminated fresh water predominantly in Africa, Asia, the Middle East, and South America. Due to the widespread prevalence of Schistosoma, co-infection with other infectious agents is common but often poorly described. Herein, we review recent literature describing the impact of Schistosoma co-infection between species and Schistosoma co-infection with blood-borne protozoa, soil-transmitted helminths, various intestinal protozoa, Mycobacterium, Salmonella, various urinary tract infection-causing agents, and viral pathogens. In each case, disease severity and, of particular interest, the immune landscape, are altered as a consequence of co-infection. Understanding the impact of schistosomiasis co-infections will be important when considering treatment strategies and vaccine development moving forward.

Transmission matrices used in epidemiologic modelling

Mixing matrices are included in infectious disease models to reflect transmission opportunities between population strata. These matrices were originally constructed on the basis of theoretical considerations and most of the early work in this area originates from research on sexually transferred diseases in the 80s, in response to AIDS. Later work in the 90s populated these matrices on the basis of survey data gathered to capture transmission risks for respiratory diseases. We provide an overview of developments in the construction of matrices for capturing transmission opportunities in populations. Such transmission matrices are useful for epidemiologic modelling to capture within and between stratum transmission and can be informed from theoretical mixing assumptions, informed by empirical evidence gathered through investigation as well as generated on the basis of data. Links to summary measures and threshold conditions are also provided.

Is income inequality linked to infectious disease prevalence? A hypothesis-generating study using tuberculosis

We study the association between infectious disease incidence and income inequality. We hypothesize that random social mixing in an income-unequal society brings into contact a) susceptible and infected poor and b) the infected-poor and the susceptible-rich, raising infectious disease incidence. We analyzed publicly available, country-level panel data for a large cross-section of countries between 1995 and 2013 to examine whether countries with elevated levels of income inequality have higher rates of pulmonary Tuberculosis (TB) incidence per capita. A "negative control" using anemia and diabetes (both non-communicable diseases and hence impervious to the hypothesized mechanism) is also applied. We find that high levels of income inequality are positively associated with tuberculosis incidence. All else equal, countries with income-Gini coefficients 10% apart show a statistically significant 4% difference in tuberculosis incidence. Income inequality had a null effect on the negative controls. Our cross-country regression results suggest that income inequality may create conditions where TB spreads more easily, and policy action to reduce income inequities could directly contribute to a reduced TB burden.