There is an emerging consensus that achieving global tuberculosis control targets will require more proactive case finding approaches than are currently used in high-incidence settings. Household contact tracing (HHCT), for which households of newly diagnosed cases are actively screened for additional infected individuals is a potentially efficient approach to finding new cases of tuberculosis, however randomized trials assessing the population-level effects of such interventions in settings with sustained community transmission have shown mixed results. One potential explanation for this is that household transmission is responsible for a variable proportion of population-level tuberculosis burden between settings. For example, transmission is more likely to occur in households in settings with a lower tuberculosis burden and where individuals mix preferentially in local areas, compared with settings with higher disease burden and more dispersed mixing. To better understand the relationship between endemic incidence levels, social mixing, and the impact of HHCT, we developed a spatially explicit model of coupled household and community transmission. We found that the impact of HHCT was robust across settings of varied incidence and community contact patterns. In contrast, we found that the effects of community contact tracing interventions were sensitive to community contact patterns. Our results suggest that the protective benefits of HHCT are robust and the benefits of this intervention are likely to be maintained across epidemiological settings.

Hurricanes produce significant, widespread, and often prolonged electrical-power outages. For example, Hurricane Irene caused more than 500 000 Long Island Power Authority customers to lose power and it took eight days to achieve 99% customer restoration. Individuals and businesses are heavily dependent on a continuous supply of electricity. Given this strong dependence on reliable electricity, individuals and private industries are increasingly putting collective pressure on regulators to require system hardening by utilities. In some cases, this has led to utility action. Conversely, many customers install a backup generator to guarantee electricity supply during disruptive events. These actions taken by individual customers affect their experiences in future storms, and are generally influenced by individuals' strength of preference for reliable power, their beliefs about the likelihood of losing power in the future, and the outcomes of their most recent experiences. However, individual action may come at the expense of collective action, whereby those who buy generators, often those with more resources available to purchase the generator, do not participate in the collective grievance, reducing the demand for overall system hardening. By using a validated power-outage forecasting model in conjunction with an agent-based model, we characterize how a community's likelihood of losing power in repeated hurricanes is affected by the complex interactions among individuals' behavioral responses in whether to engage in personal or collective action.