MUSIDH, multiple use of simulated demographic histories, a novel method to reduce computation time in microsimulation models of infectious diseases.

Microsimulation of infectious diseases requires simulation of many life histories of interacting individuals. In particular, relatively rare infections such as leprosy need to be studied in very large populations. Computation time increases disproportionally with the size of the simulated population. We present a novel method, MUSIDH, an acronym for multiple use of simulated demographic histories, to reduce computation time. Demographic history refers to the processes of birth, death and all other demographic events that should be unrelated to the natural course of an infection, thus non-fatal infections. MUSIDH attaches a fixed number of infection histories to each demographic history, and these infection histories interact as if being the infection history of separate individuals. With two examples, mumps and leprosy, we show that the method can give a factor 50 reduction in computation time at the cost of a small loss in precision. The largest reductions are obtained for rare infections with complex demographic histories.

Social distance and spatial distance are not the same, observations on the use of GIS in leprosy epidemiology.

Contacts of leprosy patients have a higher risk of developing clinical leprosy. Being a contact is defined socially, but with the introduction of geographical information systems (GIS) in infectious disease epidemiology, it is necessary to relate spatial distance to social distance. We measured the distances between patients and their socially defined contacts in northwest Bangladesh. Contact categories differ in mean distance to the index patients. Sixty-seven per cent of the high-risk contacts lived within 10 metres (m), while all low-risk contacts lived >10 m from the index patient. Classification based on intervals of spatial distance creates categories that contain contacts of different socially defined categories, illustrated by a category of people living between 10 m and 20 m consisting of 47% of high-risk contacts and 52% low-risk contacts. Classification of contacts based on the spatial distance, as performed with GIS techniques, produces other groups than with social definitions.