Preprint Article Version 1 This version is not peer-reviewed

Self-healing and Underreporting of Cases of Visceral Leishmaniasis in Bihar, India: A Mathematical Modeling-based Study

Version 1 : Received: 22 January 2019 / Approved: 23 January 2019 / Online: 23 January 2019 (08:52:50 CET)

How to cite: Prosper, O.; DebRoy, S.; Mishoe, A.; Montalvo, C.; Siddiqui, N.A.; Das, P.; Mubayi, A. Self-healing and Underreporting of Cases of Visceral Leishmaniasis in Bihar, India: A Mathematical Modeling-based Study. Preprints 2019, 2019010231 (doi: 10.20944/preprints201901.0231.v1). Prosper, O.; DebRoy, S.; Mishoe, A.; Montalvo, C.; Siddiqui, N.A.; Das, P.; Mubayi, A. Self-healing and Underreporting of Cases of Visceral Leishmaniasis in Bihar, India: A Mathematical Modeling-based Study. Preprints 2019, 2019010231 (doi: 10.20944/preprints201901.0231.v1).

Abstract

Background: Underreporting of Visceral Leishmaniasis (VL) in India remains a problem to public health controls. Effective and reliable surveillance systems are critical for monitoring disease outbreaks and public health control programs. However, in India, government surveillance systems are affected by levels of scarcity in resources and therefore, uncertainty surrounds the true incidence of asymptomatic and clinical cases, affecting morbidity and mortality rates. The State of Bihar alone contributes up to the 40\% of the worldwide VL cases. The inefficiency of surveillance systems occurs because of multiple reasons including delay in seeking health care, accessing non-authentic health care clinics, and existence of significant asymptomatic self healing infectious cases. This results in a failure of the system to adequately report true transmission rates and number of symptomatic cases that have sought medical advice (thus, high underreporting of cases). Objectives and Methods: There are several methods to estimate the extent of underreporting in the surveillance system. In this research, we use a mathematical dynamic model and two different types of data sets, namely, monthly incidence for 2003-2005 and yearly incidence from 2006-2012 from the Bihar's 21 most VL affected districts out of its 38 districts. The goals of the study are to estimate critical metrics to measure level of transmission and to evaluate the estimation process between the two data sets and 21 districts. In particularly, our focus is on (i) estimating infection transmission potential, underreporting level in incidence and proportion of self-healing cases, (ii) quantifying reproduction number of the$R_0$, and (iii) comparing underreporting incidence levels and proportion of self-healing cases between the two periods 2003-2005 and 2006-2012 and between 21 districts. Results: Our research suggests that the number of asymptomatic individuals in the population who eventually self-heal may have a significant effect on the dynamics of VL spread. The estimated mean self-healing proportion (out of all infected) is found to be $\sim 0.6$ with only 7 out of 21 affected districts having self-healing proportion less than $0.5$ for both data sets. The estimated mean underreporting level is at least $64$\% for the state of Bihar. The estimates of the basic reproduction numbers obtained are similar in magnitude for most of the districts, being in the range of (0.88, 2.79) and (0.98, 1.01) for 2003-2005 and 2006-2012, respectively. Conclusions: The estimates for the two types (monthly and yearly) of temporal data suggest that monthly data are better for estimation if less number of data points are available, however, in general, using such data set results in larger variances in parameters as compared to estimates obtained through aggregated yearly data. Estimated values of transmission related metrics are lower than those obtained from earlier analyses in the literature, and the implications of this for VL control are discussed. The spatial heterogeneity in these control metrics increases the risk of epidemics and makes the control strategies more complex.

Subject Areas

Kala-azar, Dynamical System, Inverse Problem, Spatial Analysis, Asymptomatics

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