Bridging the Gap Between Experimental Data and Model Parameterization for Chikungunya Virus Transmission Predictions

Abstract

Chikungunya virus (CHIKV) has experienced 2 major expansion events in the last decade. The most recently emerged sublineage (ECSA-V) was shown to have increased efficiency in a historically secondary vector, Aedes albopictus, leading to speculation that this was a major factor in expansion. Subsequently, a number of experimental studies focused on the vector competence of CHIKV, as well as transmission modeling efforts. Mathematical models have used these data to inform their own investigations, but some have incorrectly parameterized the extrinsic incubation period (EIP) of the mosquitoes, using vector competence data. Vector competence and EIP are part of the same process but are not often correctly reported together. Thus, the way these metrics are used for model parameterization can be problematic. We offer suggestions for bridging this gap for the purpose of standardization of reporting and to promote appropriate use of experimental data in modeling efforts.

Keywords: Aedes aegypti; Aedes albopictus; Chikungunya; arbovirus; basic reproductive number; data; extrinsic incubation period; mathematical modeling; vector competence; vectorial capacity.

Figure 1.

Five hypothetical strains of chikungunya virus (CHIKV) with differing vector competence efficiencies. Data reflect the proportion of mosquitoes exposed that are likely infectious (vector competence) at varying days after exposure.

Figure 2.

The cumulative probability of infectiousness on varying days after exposure is demonstrated by altering the value of the rate parameter (lines) on the basis of hypothetical average extrinsic incubation period (EIP) estimates.

Figure 3.

Changes in vectorial capacity (left) and the basic reproductive number (right) due to changes in the average extrinsic incubation period (EIP; all other parameters are held constant) illustrate the importance of appropriate use of vector competence data and EIP metrics.