TY - JOUR

T1 - Magnitude and frequency variations of vector-borne infection outbreaks using the Ross–Macdonald model

T2 - explaining and predicting outbreaks of dengue fever

AU - Amaku, M.

AU - Azevedo, F.

AU - Burattini, M. N.

AU - Coelho, G. E.

AU - Coutinho, F. A. B.

AU - Greenhalgh, D.

AU - Lopez, L. F.

AU - Motitsuki, R. S.

AU - Wilder-Smith, A.

AU - Massad, E.

PY - 2016/12/31

Y1 - 2016/12/31

N2 - The classical Ross–Macdonald model is often utilized to model vector-borne infections; however, this model fails on several fronts. First, using measured (or estimated) parameters, which values are accepted from the literature, the model predicts a much greater number of cases than what is usually observed. Second, the model predicts a single large outbreak that is followed by decades of much smaller outbreaks, which is not consistent with what is observed. Usually towns or cities report a number of recurrences for many years, even when environmental changes cannot explain the disappearance of the infection between the peaks. In this paper, we continue to examine the pitfalls in modelling this class of infections, and explain that, if properly used, the Ross–Macdonald model works and can be used to understand the patterns of epidemics and even, to some extent, be used to make predictions.We model several outbreaks of dengue fever and show that the variable pattern of yearly recurrence (or its absence) can be understood and explained by a simple Ross–Macdonald model modified to take into account human movement across a range of neighbourhoods within a city. In addition, we analyse the effect of seasonal variations in the parameters that determine the number, longevity and biting behaviour of mosquitoes. Based on the size of the first outbreak, we show that it is possible to estimate the proportion of the remaining susceptible individuals and to predict the likelihood and magnitude of the eventual subsequent outbreaks. This approach is described based on actual dengue outbreaks with different recurrence patterns from some Brazilian regions.

AB - The classical Ross–Macdonald model is often utilized to model vector-borne infections; however, this model fails on several fronts. First, using measured (or estimated) parameters, which values are accepted from the literature, the model predicts a much greater number of cases than what is usually observed. Second, the model predicts a single large outbreak that is followed by decades of much smaller outbreaks, which is not consistent with what is observed. Usually towns or cities report a number of recurrences for many years, even when environmental changes cannot explain the disappearance of the infection between the peaks. In this paper, we continue to examine the pitfalls in modelling this class of infections, and explain that, if properly used, the Ross–Macdonald model works and can be used to understand the patterns of epidemics and even, to some extent, be used to make predictions.We model several outbreaks of dengue fever and show that the variable pattern of yearly recurrence (or its absence) can be understood and explained by a simple Ross–Macdonald model modified to take into account human movement across a range of neighbourhoods within a city. In addition, we analyse the effect of seasonal variations in the parameters that determine the number, longevity and biting behaviour of mosquitoes. Based on the size of the first outbreak, we show that it is possible to estimate the proportion of the remaining susceptible individuals and to predict the likelihood and magnitude of the eventual subsequent outbreaks. This approach is described based on actual dengue outbreaks with different recurrence patterns from some Brazilian regions.

KW - dengue

KW - geo-spatial epidemiology

KW - mathematical models

KW - outbreak patterns

KW - vector-borne infections

UR - http://journals.cambridge.org/action/displayJournal?jid=HYG

U2 - 10.1017/S0950268816001448

DO - 10.1017/S0950268816001448

M3 - Article

VL - 144

SP - 3435

EP - 3450

JO - Epidemiology and Infection

JF - Epidemiology and Infection

SN - 0950-2688

IS - 16

ER -