A Spatio-Temporal Modeling Framework for Surveillance Data of Multiple Infectious Pathogens With Small Laboratory Validation Sets

AbstractAs countries in the Greater Mekong Sub-region (GMS) increasingly focus their malaria control and elimination efforts on forest-going populations, greater understanding of the relationship between deforestation and malaria incidence will be essential for programs to assess and meet their 2030 elimination goals. Leveraging village-level health facility surveillance data and forest cover data in a spatio-temporal modeling framework, we found evidence that deforestation is associated with short-term increases, but long-term decreases in confirmed malaria case incidence in Lao People’s Democratic Republic (Lao PDR). We identified strong associations with deforestation measured within 30 km of villages but not with deforestation in the near (10 km) and immediate (1 km) vicinity. Results appear driven by deforestation in densely forested areas and were more pronounced for infections with Plasmodium falciparum (P. falciparum) than for Plasmodium vivax (P. vivax). These findings highlight the influence of forest-going populations on malaria transmission in the GMS.

Get full-text (via PubEx)

Spatio-temporal associations between deforestation and malaria incidence in Lao PDR

eLife ◽

10.7554/elife.56974 ◽

2021 ◽

Vol 10 ◽

Cited By ~ 1

Author(s):

Francois Rerolle ◽

Emily Dantzer ◽

Andrew A Lover ◽

John M Marshall ◽

Bouasy Hongvanthong ◽

...

Keyword(s):

Forest Cover ◽

Malaria Incidence ◽

Lao Pdr ◽

Short Term ◽

Modeling Framework ◽

Temporal Modeling ◽

Forested Areas ◽

Spatio Temporal ◽

The Relationship

As countries in the Greater Mekong Sub-region (GMS) increasingly focus their malaria control and elimination efforts on reducing forest-related transmission, greater understanding of the relationship between deforestation and malaria incidence will be essential for programs to assess and meet their 2030 elimination goals. Leveraging village-level health facility surveillance data and forest cover data in a spatio-temporal modeling framework, we found evidence that deforestation is associated with short-term increases, but long-term decreases confirmed malaria case incidence in Lao People’s Democratic Republic (Lao PDR). We identified strong associations with deforestation measured within 30 km of villages but not with deforestation in the near (10 km) and immediate (1 km) vicinity. Results appear driven by deforestation in densely forested areas and were more pronounced for infections with Plasmodium falciparum (P. falciparum) than for Plasmodium vivax (P. vivax). These findings highlight the influence of forest activities on malaria transmission in the GMS.

Get full-text (via PubEx)

Non-stationary Spatio-Temporal Modeling of COVID-19 Progression in The U.S.

10.1101/2020.09.14.20194548 ◽

2020 ◽

Author(s):

Yue Bai ◽

Abolfazl Safikhani ◽

George Michailidis

Keyword(s):

Transmission Rate ◽

Synthetic Data ◽

Mitigation Strategies ◽

Modeling Framework ◽

Temporal Modeling ◽

Empirical Performance ◽

Important Challenge ◽

Auto Regressive ◽

Break Points ◽

Spatio Temporal

The fast transmission rate of COVID-19 worldwide has made this virus the most important challenge of year 2020. Many mitigation policies have been imposed by the governments at different regional levels (country, state, county, and city) to stop the spread of this virus. Quantifying the effect of such mitigation strategies on the transmission and recovery rates, and predicting the rate of new daily cases are two crucial tasks. In this paper, we propose a modeling framework which not only accounts for such policies but also utilizes the spatial and temporal information to characterize the pattern of COVID-19 progression. Specifically, a piecewise susceptible-infected-recovered (SIR) model is developed while the dates at which the transmission/recover rates change significantly are defined as "break points" in this model. A novel and data-driven algorithm is designed to locate the break points using ideas from fused lasso and thresholding. In order to enhance the forecasting power and to describe additional temporal dependence among the daily number of cases, this model is further coupled with spatial smoothing covariates and vector auto-regressive (VAR) model. The proposed model is applied to several U.S. states and counties, and the results confirm the effect of "stay-at-home orders" and some states' early "re-openings" by detecting break points close to such events. Further, the model performed satisfactorily short-term forecasts of the number of new daily cases at regional levels by utilizing the estimated spatio-temporal covariance structures. Finally, some theoretical results and empirical performance of the proposed methodology on synthetic data are reported which justify the good performance of the proposed method.

Get full-text (via PubEx)

Characterization of Regional Influenza Seasonality Patterns in China and Implications for Vaccination Strategies: Spatio-Temporal Modeling of Surveillance Data

PLoS Medicine ◽

10.1371/journal.pmed.1001552 ◽

2013 ◽

Vol 10 (11) ◽

pp. e1001552 ◽

Cited By ~ 128

Author(s):

Hongjie Yu ◽

Wladimir J. Alonso ◽

Luzhao Feng ◽

Yi Tan ◽

Yuelong Shu ◽

...

Keyword(s):

Surveillance Data ◽

Temporal Modeling ◽

Vaccination Strategies ◽

Spatio Temporal ◽

Influenza Seasonality

Get full-text (via PubEx)

Pyramidal Framework: Guidance for the Next Generation of GIS Spatial-Temporal Models

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10030188 ◽

2021 ◽

Vol 10 (3) ◽

pp. 188

Author(s):

Cyril Carré ◽

Younes Hamdani

Keyword(s):

Conceptual Models ◽

Future Generations ◽

Temporal Data ◽

Temporal Modeling ◽

Temporal Models ◽

Gis Data ◽

Spatio Temporal ◽

New Interpretation ◽

Conventional Literature

Over the last decade, innovative computer technologies and the multiplication of geospatial data acquisition solutions have transformed the geographic information systems (GIS) landscape and opened up new opportunities to close the gap between GIS and the dynamics of geographic phenomena. There is a demand to further develop spatio-temporal conceptual models to comprehensively represent the nature of the evolution of geographic objects. The latter involves a set of considerations like those related to managing changes and object identities, modeling possible causal relations, and integrating multiple interpretations. While conventional literature generally presents these concepts separately and rarely approaches them from a holistic perspective, they are in fact interrelated. Therefore, we believe that the semantics of modeling would be improved by considering these concepts jointly. In this work, we propose to represent these interrelationships in the form of a hierarchical pyramidal framework and to further explore this set of concepts. The objective of this framework is to provide a guideline to orient the design of future generations of GIS data models, enabling them to achieve a better representation of available spatio-temporal data. In addition, this framework aims at providing keys for a new interpretation and classification of spatio-temporal conceptual models. This work can be beneficial for researchers, students, and developers interested in advanced spatio-temporal modeling.

Get full-text (via PubEx)

Spatio-Temporal Modeling on Village Land Feature Change

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.204-208.2721 ◽

2012 ◽

Vol 204-208 ◽

pp. 2721-2725

Author(s):

Hua Ji Zhu ◽

Hua Rui Wu

Keyword(s):

Real World ◽

Temporal Change ◽

End Users ◽

Temporal Modeling ◽

The Real ◽

Change Type ◽

Feature Change ◽

Spatio Temporal ◽

The Village ◽

Land Changes

Village land continually changes in the real world. In order to keep the data up-to-date, data producers need update the data frequently. When the village land data are updated, the update information must be dispensed to the end-users to keep their client-databases current. In the real world, village land changes in many forms. Identifying the change type of village land (i.e. captures the semantics of change) and representing them in the data world can help end-users understand the change commonly and be convenient for end-users to integrate these change information into their databases. This work focuses on the model of the spatio-temporal change. A three-tuple model CAR for representing the spatio-temporal change is proposed based on the village land feature set before change and the village land feature set after change, change type and rules. In this model, the C denotes the change type. A denotes the attribute set; R denotes the judging rules of change type. The rule is described by the IF－THEN expressions. By the operations between R and A, the C is distinguished. This model overcomes the limitations of current methods. And more, the rules in this model can be easy realized in computer program.

Get full-text (via PubEx)