Using Hawkes Processes to model imported and local malaria cases in near-elimination settings

Developing new methods for modelling infectious diseases outbreaks is important for monitoring transmission and developing policy. In this paper we propose using semi-mechanistic Hawkes Processes for modelling malaria transmission in near-elimination settings. Hawkes Processes are well founded mathematical methods that enable us to combine the benefits of both statistical and mechanistic models to recreate and forecast disease transmission beyond just malaria outbreak scenarios. These methods have been successfully used in numerous applications such as social media and earthquake modelling, but are not yet widespread in epidemiology. By using domain-specific knowledge, we can both recreate transmission curves for malaria in China and Eswatini and disentangle the proportion of cases which are imported from those that are community based.

Using Hawkes Processes to model imported and local malaria cases in near-elimination settings

Developing new methods for modelling infectious diseases outbreaks is important for mon- itoring transmission and developing policy. In this paper we propose using semi-mechanistic Hawkes Processes for modelling malaria transmission in near-elimination settings. Hawkes Processes are mathematical methods that enable us to combine the benefits of both sta- tistical and mechanistic models to recreate and forecast disease transmission beyond just malaria outbreak scenarios. These methods have been successfully used in social media and earthquake modelling, but are not yet widespread in epidemiology. By using domain-specific knowledge, we can both recreate transmission curves for malaria in China and Swaziland and disentangle the proportion of cases which are imported from those that are community based.

Kite - bridging InSAR displacement analysis and earthquake modelling: the 2019 Ridgecrest earthquakes

<p>We present a modular open-source software framework - Kite (http://pyrocko.org) - for rapid post-processing of spaceborne InSAR-derived surface displacement maps. The software enables swift parametrisation, post-processing and sub-sampling of the displacement measurements that are compatible with common InSAR processors (e.g. SNAP, GAMMA, ISCE, etc.) and online processing centers delivering unrwapped InSAR data products, such as NASA ARIA or LiCSAR. The post-processing capabilities include removal of first-order atmospheric phase delays through elevation correlation estimations and regional atmospheric phase screen (APS) estimations based on atmospheric models (GACOS), masking of displacement data, adaptive data sub-sampling using quadtree decomposition and data error covariance estimation.</p><p>Kite datasets integrate into forward modelling and optimisation frameworks Grond (Heiman et al., 2019) and BEAT (Vasyura-Bathke et al., 2019), both software packages aim to ease and streamline the joint optimisation of earthquake parameters from InSAR and GPS data together with seismological waveforms. These data combinations will improve the estimation of earthquake rupture parameters. Establishing this data processing software framework we want to bridge the gap between InSAR processing software and seismological modelling frameworks, to contribute to a timely and better understanding of earthquake kinematics. This approach paves the way to automated inversion of earthquake models incorporating space-borne InSAR data.</p><p>Under development is the processing of InSAR displacement time series data to link simultaneous modelling of co- and post-seismic transient deformation processes from InSAR observations to physical earthquake cycle models.</p><p>We demonstrate the framework’s capabilities with an analysis of the 2019 Ridgecrest earthquakes from InSAR surface displacements (provided by NASA ARIA) combined with GNSS displacements using the Bayesian bootstrapping strategy from the Grond inverse modelling tool.</p>

Grouting performance of the Pandan Duri dam shacked by Lombok earthquake 2018

The Pandan Duri dam has an important role in water supply in the East Lombok District. Unfortunately, Lombok Island is located in a relatively high seismic risk area, as earthquakes occurred in 2018 have ruined almost one third of the island and destroyed thousands of buildings. The Pandan Duri dam is one of many infrastructures shacked by the earthquake. Modelling the dam shows that the grouting installation of the dam has supported the dam after the earthquake. The factor of safety (FoS) of the dam was still 1. 76 after the earthquake for the flood water level conditions; but, the displacements could be significant to the stability of the dam, as the dam might displace up to 1. 64 m. Although, the current grouting support system might provide the stability to the dam during earthquake events, the dam will certainly require further stability enhancement and close monitoring for possible similar events in the future.

Rapid post-earthquake modelling of coseismic landslide intensity and distribution for emergency response decision support

Current methods to identify coseismic landslides immediately after an earthquake using optical imagery are too slow to effectively inform emergency response activities. Issues with cloud cover, data collection and processing, and manual landslide identification mean even the most rapid mapping exercises are often incomplete when the emergency response ends. In this study, we demonstrate how traditional empirical methods for modelling the total distribution and relative intensity (in terms of point density) of coseismic landsliding can be successfully undertaken in the hours and days immediately after an earthquake, allowing the results to effectively inform stakeholders during the response. The method uses fuzzy logic in a GIS (Geographic Information Systems) to quickly assess and identify the location-specific relationships between predisposing factors and landslide occurrence during the earthquake, based on small initial samples of identified landslides. We show that this approach can accurately model both the spatial pattern and the number density of landsliding from the event based on just several hundred mapped landslides, provided they have sufficiently wide spatial coverage, improving upon previous methods. This suggests that systematic high-fidelity mapping of landslides following an earthquake is not necessary for informing rapid modelling attempts. Instead, mapping should focus on rapid sampling from the entire affected area to generate results that can inform the modelling. This method is therefore suited to conditions in which imagery is affected by partial cloud cover or in which the total number of landslides is so large that mapping requires significant time to complete. The method therefore has the potential to provide a quick assessment of landslide hazard after an earthquake and may therefore inform emergency operations more effectively compared to current practice.

Analogue earthquakes and seismic cycles: experimental modelling across timescales

Earth deformation is a multi-scale process ranging from seconds (seismic deformation) to millions of years (tectonic deformation). Bridging short- and long-term deformation and developing seismotectonic models has been a challenge in experimental tectonics for more than a century. Since the formulation of Reid's elastic rebound theory 100 years ago, laboratory mechanical models combining frictional and elastic elements have been used to study the dynamics of earthquakes. In the last decade, with the advent of high-resolution monitoring techniques and new rock analogue materials, laboratory earthquake experiments have evolved from simple spring-slider models to scaled analogue models. This evolution was accomplished by advances in seismology and geodesy along with relatively frequent occurrences of large earthquakes in the past decade. This coincidence has significantly increased the quality and quantity of relevant observations in nature and triggered a new understanding of earthquake dynamics. We review here the developments in analogue earthquake modelling with a focus on those seismotectonic scale models that are directly comparable to observational data on short to long timescales. We lay out the basics of analogue modelling, namely scaling, materials and monitoring, as applied in seismotectonic modelling. An overview of applications highlights the contributions of analogue earthquake models in bridging timescales of observations including earthquake statistics, rupture dynamics, ground motion, and seismic-cycle deformation up to seismotectonic evolution.