New methodologies for the estimation of population vulnerability to diseases: a case study of Lassa fever and Ebola in Nigeria and Sierra Leone

Abstract

Public health practitioners require measures to evaluate how vulnerable populations are to diseases, especially for zoonoses (i.e. diseases transmitted from animals to humans) given their pandemic potential. These measures would be valuable to support strategic and operational decision making and allocation of resources. Although vulnerability is well defined for natural hazards, for public health threats the concept remains undetermined. Here, we develop new methodologies to: (i) quantify the impact of zoonotic diseases and the capacity of countries to cope with these diseases, and (ii) combine these two measures (impact and capacity) into one overall vulnerability indicator. The adaptive capacity is calculated from estimations of disease mortality, although the method can be adapted for diseases with no or low mortality but high morbidity. As an example, we focused on the vulnerability of Nigeria and Sierra Leone to Lassa Fever and Ebola. We develop a simple analytical form that can be used to estimate vulnerability scores for different spatial units of interest, e.g. countries or regions. We show how some populations can be highly vulnerable despite low impact threats. We finally outline future research to more comprehensively inform vulnerability with the incorporation of relevant factors depicting local heterogeneities (e.g. bio-physical and socio-economic factors). This article is part of the theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control'. This theme issue is linked with the earlier issue 'Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes'.

Keywords: adaptive capacity; impact; mathematical modelling; zoonosis.

Figure 1.

Time-dependent vulnerability to Lassa fever for Sierra Leone (a) and Nigeria (b) during recorded epidemics. Continuous dark red lines: severe situations; grey areas: 95% confidence intervals for the severe situations; continuous blue lines: general situation; orange dashed-lines: overall, crude estimates of vulnerability for severe situation based on an observed case-fatality rate of 15% among patients hospitalized with severe cases of Lassa fever [41], i.e. V = 100/(100 − 15); black dashed-lines: overall, crude estimate of vulnerability for general situation based on an overall case-fatality rate of 1% [41], i.e. V = 100/99. Data from the first month were removed to avoid potential death cases associated with infections occurred the month before and not detected.

Figure 2.

Time-dependent vulnerability to Ebola for Sierra Leone during recorded epidemics. Continuous dark red line: severe situations; grey area: 95% confidence interval for the severe situations. Data from the first month were removed to avoid potential death cases associated with infections that occurred the month before and were not detected.

Figure 3.

Cumulative number of (a) detected and (b) recovered Ebola cases in Sierra Leone. Data from the first month were removed to avoid potential death cases associated with infections that occurred the month before and were not detected.

Figure 4.

(a) Number of confirmed cases and (b) vulnerability for Lassa fever for different states in Nigeria based on cases up to 22 April 2018. For some states (Abia, Benue, Enugu, Gombe, Kaduna, Lagos) the vulnerability was undefined (as adaptive capacity was zero) and therefore not included in the analysis. The vertical lines represent the 95% confidence intervals. FCT, Federal Capital Territory. (Online version in colour.)