Data and model operations in computational sciences. The examples of computational embryology and epidemiology

Computer models and simulations have become, since the 1960s, an essential instrument for scientific inquiry and political decision making in several fields, from climate to life and social sciences. Philosophical reflection has mainly focused on the ontological status of the computational modeling, on its epistemological validity and on the research practices it entails. But in computational sciences, the work on models and simulations are only two steps of a longer and richer process where operations on data are as important as, and even more time and energy-consuming than modeling itself. Drawing on two study cases – computational embryology and computational epidemiology –, this article contributes to fill the gap by focusing on the operations of producing and re-using data in computational sciences. The different phases of the scientific and artisanal work of modelers include data collection, aggregation, homogenization, assemblage, analysis and visualization. The article contributes to deconstruct the ideas that data are self-evident informational aggregates and that data-driven approaches are exempted from theoretical work. More importantly, the paper stresses the fact that data are constructed and theory-laden not only in their fabrication, but also in their reusing.

Inferring Urban Social Networks from Publicly Available Data

The definition of suitable generative models for synthetic yet realistic social networks is a widely studied problem in the literature. By not being tied to any real data, random graph models cannot capture all the subtleties of real networks and are inadequate for many practical contexts—including areas of research, such as computational epidemiology, which are recently high on the agenda. At the same time, the so-called contact networks describe interactions, rather than relationships, and are strongly dependent on the application and on the size and quality of the sample data used to infer them. To fill the gap between these two approaches, we present a data-driven model for urban social networks, implemented and released as open source software. By using just widely available aggregated demographic and social-mixing data, we are able to create, for a territory of interest, an age-stratified and geo-referenced synthetic population whose individuals are connected by “strong ties” of two types: intra-household (e.g., kinship) or friendship. While household links are entirely data-driven, we propose a parametric probabilistic model for friendship, based on the assumption that distances and age differences play a role, and that not all individuals are equally sociable. The demographic and geographic factors governing the structure of the obtained network, under different configurations, are thoroughly studied through extensive simulations focused on three Italian cities of different size.

An Analytic Framework for Effective Public Health Program Design Using Correctional Facilities

We present a decision analytic framework that uses a mathematical model of Chlamydia trachomatis transmission dynamics in two interacting populations using ordinary differential equations. A public health survey informs model parametrization, and analytical findings guide the computational design of the decision-making process. The potential impact of jail-based screen-treat (S-T) programs on community health outcomes is presented. Numerical experiments are conducted for a case study population to quantify the effect and evaluate the cost-effectiveness of considered interventions. Numerical experiments show the effectiveness of increased jail S-T rates on community cases when resources for a community S-T program stays constant. Although this effect decreases when higher S-T rates are in place, jail-based S-T programs are cost-effective relative to community-based programs. Summary of Contribution: Public health programs have been developed to control community-wide infectious diseases and to reduce prevalence of sexually transmitted diseases (STD). These programs can consist of screening and treatment of diseases and behavioral interventions. Public correctional facilities play an important role in operational execution of these public health programs. However, because of lack of capacity and resources, public health programs using correctional facilities are questioned by policy-makers in terms of their costs and benefits. In this article, we present an analytical framework using a computational epidemiology model for supporting public health policy making. The system represents the dynamics of Chlamydia trachomatis transmission in two interacting populations, with an ordinary differential equations-based simulation model. The theoretical epidemic control conditions are derived and numerically tested, which guide the design of simulation experiments. Then cost-effectiveness of the potential policies is analyzed. We also present an extensive sensitivity analyses on model parameters. This study contributes to the computational epidemiology literature by presenting an analytical framework to guide effective simulation experimentation for policy decision making. The presented methodology can be applied to other complex policy and public health problems.

Fast and principled simulations of the SIR model on temporal networks

The Susceptible–Infectious–Recovered (SIR) model is the canonical model of epidemics of infections that make people immune upon recovery. Many of the open questions in computational epidemiology concern the underlying contact structure’s impact on models like the SIR model. Temporal networks constitute a theoretical framework capable of encoding structures both in the networks of who could infect whom and when these contacts happen. In this article, we discuss the detailed assumptions behind such simulations—how to make them comparable with analytically tractable formulations of the SIR model, and at the same time, as realistic as possible. We also present a highly optimized, open-source code for this purpose and discuss all steps needed to make the program as fast as possible.