Publications
Explainable machine learning models to analyse maternal health
July 2023 | Data & Knowledge Engineering
Shivshanker Singh Patel
Maternal health is a significant public health concern for globe and many developing countries. A country like India (with large population), there are considerable disparities in maternal health service utilisation and maternal mortality within and across states. A more than a general healthcare operational policy would suffice, but a precision healthcare strategy would be needed. This article focused on explainable machine learning models that can precisely advise health care intervention policy and medical treatment to an administrative unit rather than a generic policy suggestion for improving maternal health. This study presents an exhaustive list of factors associated with Maternal Mortality Rate (MMR) and a series of explainable AI models. One of models uses CART heuristics to categorise districts (administrative boundaries) into lower and higher MMR classes. Another explainable model, Shapley Additive Explanations (SHAP), used SVM, ANN, boosting, and random forest machine learning models to investigate higher and lower MMR regions. Further, an Explainable Boosting Machine (EBM) also used, and the results are compared for policy suggestions. Some of the ignored features from general social science studies, such as topography and agro-climatic zone characteristics of a particular district, may be crucial in the analysis. Moreover, health infrastructure, insurance, and other factors also influence policymaking. This predictive and explainable work has significant implications for precision healthcare policy design to improve maternal health compared to a broader policy approach.
A generalized epidemiological model with dynamic and asymptomatic population
August 2022 | Statistical Methods in Medical Research
Anirban Ghatak, Shivshanker Singh Patel, Soham Bonnerjee, Subhrajyoty Roy
In this paper, we develop an extension of compartmental epidemiological models which is suitable for COVID-19. The model presented in this paper comprises seven compartments in the progression of the disease. This model, named as the SINTRUE (Susceptible, Infected and pre-symptomatic, Infected and Symptomatic but Not Tested, Tested Positive, Recorded Recovered, Unrecorded Recovered, and Expired) model. The proposed model incorporates transmission due to asymptomatic carriers and captures the spread of the disease due to the movement of people to/from different administrative boundaries within a country. In addition, the model allows estimating the number of undocumented infections in the population and the number of unrecorded recoveries. The associated parameters in the model can help architect the public health policy and operational management of the pandemic. The results show that the testing rate of the asymptomatic patients is a crucial parameter to fight against the pandemic. The model is also shown to have a better predictive capability than the other epidemiological models.
Informal-contract farming in an agriculture supply chain: a game-theoretic analysis
March 2022 | International Journal of Operational Research
Shivshanker Singh Patel
A contract in an agriculture supply-chain under market uncertainty leads to renege. Specially, when the contract enforcement cost is not very high it is prone to collapse. In this paper, a set of game theoretical models have been employed to analyse renege of the contract farming (informal-contract). To start with a normal form game-theoretic model with pure strategies has been utilised to model the price risk of the market and determine the outcomes for the players (firm and farmer); subsequently, a mixed strategy model has been studied. Owing to incomplete information under the informal-contract, a Bayesian Nash equilibrium and mixed strategy Bayesian Nash equilibrium have also been analysed. The results have been explained with an example of tomato contract farming of Southern India. From the business standpoint the results and renegotiation framework presented in this paper can be utilised to avoid a renege and dispute in the contract farming.
A bargaining model for sharing water in a river with negative externality
August 2021 | OPSEARCH
Shivshanker Singh Patel, Parthasarathy Ramachandran
This article is focused on the problem of river sharing in the presence of pollution as a negative externality between two riparian states (agents). In this paper, a market-based contract mechanism is presented; it can address the issue of negative externality imposed by an upstream agent on the downstream agents while sharing a river. The proposed mechanism incorporates a penalty for pollution and also incentives for trading water between upstream and downstream agent. The mechanism introduces a new concept of negative water as penalty against pollution for an upstream agent in a contract for water sharing. The contract is analyzed by a market-based bargaining model to determine a negotiated treaty between the upstream agent and the downstream agent. The results show the characterization of agents with regard to agreement points for negotiated treaty. Also, it shows that an equilibrium exists for a unique solution that makes both the agents better off. The model discussed in this paper can be easily applied to any transboundary river conflict where pollution plays an important role.
A Generalized Epidemiological Model for COVID-19 with Dynamic and Asymptomatic Population
November 2020 | arXiv e-Print
Anirban Ghatak, Shivshanker Singh Patel, Soham Bonnerjee, Subhrajyoty Roy
In this paper, we develop an extension of standard epidemiological models, suitable for COVID-19. This extension incorporates the transmission due to pre-symptomatic or asymptomatic carriers of the virus. Furthermore, this model also captures the spread of the disease due to the movement of people to/from different administrative boundaries within a country. The model describes the probabilistic rise in the number of confirmed cases due to the concomitant effects of (incipient) human transmission and multiple compartments. The associated parameters in the model can help architect the public health policy and operational management of the pandemic. For instance, this model demonstrates that increasing the testing for symptomatic patients does not have any major effect on the progression of the pandemic, but testing rate of the asymptomatic population has an extremely crucial role to play. The model is executed using the data obtained for the state of Chhattisgarh in the Republic of India. The model is shown to have significantly better predictive capability than the other epidemiological models. This model can be readily applied to any administrative boundary (state or country). Moreover, this model can be applied for any other epidemic as well.
Forecasting health of complex IT systems using system log data
February 2020 | Journal of Banking and Financial Technology
Shivshanker Singh Patel
Predicting the health of digital infrastructure is a vital issue to minimize downtime to maintain a high service level. This research work has applied predictive analytics to forecast future health of complex-IT (Information Technology) based infrastructure. Every subset of a complex IT infrastructure is at the level of a single machine, that tracks the run-time status of the system and generates electronic messages, error events, and further manual massages as ticket logs. This research has suggested a 3-step method to build a novel predictive analytics model using text mining algorithm for extracting features from the log data. Then, it provides a model for selecting the critical devices in the system and predicting their failure. The final step suggests a forecasting model to predict the health of infrastructure for a given timestamp. The models in the second step of this integrated approach are built using algorithms such as association rules and rank based algorithm. The time-series model is built using a machine learning methods (ANN and SVR). This approach can be readily applied to many other types of information technology-based medical, banking, energy infrastructure, and other applications.
A Bilateral River Bargaining Problem with Negative Externality
Dec 2019 | arXiv e-Print
Shivshanker Singh Patel, Parthasarathy Ramachandran
This article is addressing the problem of river sharing between two agents along a river in the presence of negative externalities. Where, each agent claims river water based on the hydrological characteristics of the territories. The claims can be characterized by some international framework (principles) of entitlement. These international principles are appears to be inequitable by the other agents in the presence of negative externalities. The negotiated treaties address sharing water along with the issue of negative externalities imposed by the upstream agent on the downstream agents. The market based bargaining mechanism is used for modeling and for characterization of agreement points.
An optimization model and policy analysis of water allocation for a river basin
May 2017 | Sustainable Water Resources Management
Shivshanker Singh Patel, Parthasarathy Ramachandran
The problem of inter-sectoral water allocation is investigated for the utilizable water in the Cauvery river basin in the state of Karnataka, India. This paper aims to maximize the total benefit of available and utilizable water while trying to ensure a certain basic water right for every individual. It also aims to meet irrigation requirements as put forward by government (central or state) in drought contingency plan. In this context, a novel nonlinear optimization model is developed which utilizes hydro-agro-economic data collected from multiple sources. This optimization model allocates the available water among different competing sectors which includes municipality, industries and agriculture. Furthermore, the sensitivity analysis evaluates the economic impact of different parameters of competing demands such as water availability, population and basic water right (quantity). The results of this study reveal that the basic water right for essential needs can be ensured with integrated management of available surface water resources. This novel optimization model and policy analysis can be readily applied to other river basins across the globe.
June 2014 | Water Resources Management
Shivshanker Singh Patel, Parthasarathy Ramachandran
Models of river flow time series are essential in efficient management of a river basin. It helps policy makers in developing efficient water utilization strategies to maximize the utility of scarce water resource. Time series analysis has been used extensively for modeling river flow data. The use of machine learning techniques such as support-vector regression and neural network models is gaining increasing popularity. In this paper we compare the performance of these techniques by applying it to a long-term time-series data of the inflows into the Krishnaraja Sagar reservoir (KRS) from three tributaries of the river Cauvery. In this study flow data over a period of 30 years from three different observation points established in upper Cauvery river sub-basin is analyzed to estimate their contribution to KRS. Specifically, ANN model uses a multi-layer feed forward network trained with a back-propagation algorithm and support vector regression with epsilon intensive–loss function is used. Auto-regressive moving average models are also applied to the same data. The performance of different techniques is compared using performance metrics such as root mean squared error (RMSE), correlation, normalized root mean squared error (NRMSE) and Nash-Sutcliffe Efficiency (NSE).