Modeling the spread of infectious diseases in space and time needs to take care of complex dependencies and uncertainties. Machine learning methods, and neural networks, in particular, are useful in modeling this sort of complex problems, although they generally lack of probabilistic interpretations. We propose a neural network method embedded in a Bayesian framework for modeling and predicting the number of cases of infectious diseases in areal units. A key feature is that our combined model considers the impact of human movement on the spread of the infectious disease, as an additional random factor to the also considered spatial neighborhood and temporal correlation components. Our model is evaluated over a COVID-19 dataset for 245 health zones of Castilla-Leon (Spain). The results show that a Bayesian model informed by a neural network method is generally able to predict the number of cases of COVID-19 in both space and time, with the human mobility factor having a strong influence on the model, together with the number of infections and deaths in nearby areas.

Modeling the behavior and spread of infectious diseases on space and time is key in devising public policies for preventive measures. This behavior is so complex that there are lots of uncertainties in both the data and in the process itself. We argue here that these uncertainties should be taken into account in the modeling strategy. Machine learning methods, and neural networks, in particular, are useful in modeling this sort of complex problems, although they generally lack of probabilistic interpretations. We thus present here a neural network method embedded in a Bayesian framework for modeling and predicting the number of cases of infectious diseases in areal units. A key feature is that our combined model considers the impact of human movement on the spread of the infectious disease, as an additional random factor to the also considered spatial neighborhood and temporal correlation components. Our model is evaluated over a COVID-19 dataset for 245 health zones of Castilla-Leon (Spain). The results show that a Bayesian model informed by a neural network method is generally able to predict the number of cases of COVID-19 in both space and time, with the human mobility factor having a strong influence on the model.

The Survey Department of Nepal has started transformation of cadastral management from offline to centralized online system. This has enabled the department to provide the online services to the clients beyond the physical boundaries of Survey Offices. This paper elaborates the present status of online service delivery in different survey offices based on recent policy, institutions and their applications. Data analysis shows that Nepal Land Information System (NeLIS) supports basic norms and values of online service delivery effectively and has become a major milestone in the e-land administration. The provision of assigning different roles and sections inside a survey office has made the system more secure, reliable and responsive. Recently, directives of digital land surveying, mapping and administration for public service delivery have been formulated to address the legal aspect of service delivery. The provision of online application for map, field book and plot register print through https://www.merokitta. dos.gov.np has simplified the working procedure for general public and institutional users too. The expansion of this system in more survey offices along with additional and more advanced features are necessary. Similarly, legal provision of data sharing between Land Records Information Management System (LRIMS) and NeLIS in a meaningful way is also essential in the days to come.