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The Philippines, as part of the Circum-Pacific belt, is considered as one of the most seismically active countries in the world. Earthquake occurrence is frequent and its effects vary depending on its size. Understanding how the occurrences happen is therefore important. Stochastic models of earthquake occurrence have been used to study seismic activities in various active earthquake zones globally. In this paper, we apply Poisson hidden Markov models (PHMM) using the January 1, 1960 to January 20, 2019 earthquake data of Metro Manila, Philippines. The parameters in the models are estimated using expectation-maximization (EM) algorithm. We determine using various statistical tests that the 5-state PHMM best represents the earthquake data and implement bootstrap algorithm to validate the acceptability of its parameter estimates. Moreover, we investigate the forecasting capability of the 5-state PHMM by comparing it to the ARIMA model. Using unscaled mean bounded relative absolute error (UMBRAE), we find that the 5-state PHMM gives closer one-step ahead forecasts and is a better forecasting model for the considered data. [ABSTRACT FROM AUTHOR]

After an earthquake, the goal of emergency rescue work is to minimize the number of casualties and save lives. The most important initial task is the rapid assessment of the number of earthquake casualties. At present, the rapid assessment of casualties relies primarily on census data rather than real-time population data in an earthquake area. Features of the census data, such as their low accuracy and time lag, can easily cause large errors in the assessment result; thus, the assessment result cannot always reflect the actual situation in an earthquake area. In this paper, we use phone signal data to construct a population density model and estimate the real-time population in a seismic area. The results show that the estimated population is consistent with the actual data in the study area. Finally, we obtain a real-time population distribution map of the study area. The real-time distribution of the population is consistent with the actual situation based on the economic and social development of the study area. The population in an urban area is relatively dense, whereas that in a rural area is less dense. The results show that phone signal data can play a useful role in estimating the real-time population when an earthquake occurs and can be used to support earthquake emergency rescue work. [ABSTRACT FROM AUTHOR]