Paula Moraga

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<p class="text-center" style = 'font-size: 54px; line-height:1.3; font-weight:bold'>Geospatial Risk Models for Decision-Making in<br> Global Health</p>
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Paula Moraga, Ph.D.<br>
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Asst. Professor of Statistics
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King Abdullah University of Science and Technology (KAUST), Saudi Arabia
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<a href='http://twitter.com/Paula_Moraga_' target='_blank'> <i class='fa fa-twitter fa-fw'></i>&nbsp; @Paula_Moraga_</a><br>
<a href='https://Paula-Moraga.github.io/' target='_blank'> <i class='fa fa-globe fa-fw'></i>&nbsp; www.PaulaMoraga.com</a><br>
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# Who am I?

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<p style="font-size:25px"><b>Paula Moraga, Ph.D.</b></p>
Assistant Professor of Statistics <br>for Public Health at KAUST<br><br>
PI Geospatial Statistics and Health Surveillance Research Group<br><br>
<img src="./figures/Statistics at KAUST_Logo for digital use_small.png" height = "120" alt = "a png">&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
<img src="./figures/logogeohealth.png" height = "120" alt = "a png">
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<img class = "circular--square" src="./figures/paula.png" width = "200" alt = "a png"><br><br>
<a href='http://twitter.com/Paula_Moraga_' target='_blank'><i class='fa fa-twitter fa-fw'></i>&nbsp; @Paula_Moraga_</a><br>
<a href='https://Paula-Moraga.github.io/' target='_blank'><i class='fa fa-globe fa-fw'></i>&nbsp; www.PaulaMoraga.com</a><br>
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<i class='fa fa-map-marked-alt fa-fw'></i>&nbsp; Geospatial data analysis, statistical modeling<br>
<i class='fa fa-hospital fa-fw'></i>&nbsp; Spatial epidemiology, disease mapping, health surveillance<br>
<i class='fa fa-laptop fa-fw'></i>&nbsp; Development of R packages and interactive visualization applications<br>
<i class='fa fa-book fa-fw'></i>&nbsp; Author book Geospatial Health Data http://bit.ly/bookgeo<br>
<i class='fa fa-graduation-cap fa-fw'></i>&nbsp; PhD Mathematics, Valencia. Master's Biostatistics, Harvard<br>

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## Book

Geospatial Health Data: Modeling and Visualization with R-INLA and Shiny (2019, Chapman & Hall/ CRC Press)    
http://www.paulamoraga.com/book-geospatial/

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- Manipulate and transform point, areal and raster data,<br> and create maps with R

- Fit and interpret Bayesian spatial and spatio-temporal models with INLA and SPDE

- Model disease risk and quantify risk factors in different settings

- Interactive visualizations, reproducible reports, dashboards, and Shiny apps

- Health examples but methods useful to analyze georeferenced data in other fields such as ecology or criminology

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# Bayesian Geospatial Models for<br> Tropical Disease Mapping

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## Lymphatic filariasis in sub-Saharan Africa

## Leptospirosis in Brazil

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# Lymphatic filariasis in sub-Saharan Africa

### Modelling the distribution and transmission intensity of lymphatic filariasis in sub-Saharan Africa prior to scaling up interventions: integrated use of geostatistical and mathematical modelling

### [Moraga, et al., Parasites & Vectors, 8:560, 2015](https://doi.org/10.1186/s13071-015-1166-x)

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# Geospatial modelling of lymphatic filariasis prevalence in sub-Saharan Africa

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Lymphatic filariasis (LF) caused by microscopic worms and transmitted by mosquitoes

Disfigurement and disabilities due to lymphedema, elephantiasis and hydrocele

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<img src="./figures/lfAedesaegypti.jpg" style="width:80%;"/>
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# Geospatial modelling of lymphatic filariasis prevalence in sub-Saharan Africa

Main strategy against the disease is **Mass Drug Administration**
(recommended to entire populations in regions where prevalence
exceeds 1% annually for at least five years)

Resources are limited and need to decide which are the areas most in need

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# LF prevalence surveys in sub-Saharan Africa

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3197 surveys 1990-2014
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# Predict local LF prevalence

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# Predict local LF prevalence

`$$Y_i|P(\boldsymbol{x}_i)\sim \mbox{Binomial} (n_i, P(\boldsymbol{x}_i))$$`
`$$\mbox{logit}(P(\boldsymbol{x}_i))  = \boldsymbol{z}_i \boldsymbol{\beta} + S(\boldsymbol{x}_i) + u_i$$`

Covariates based on characteristics known to affect LF transmission
(precipitation, vegetation, elevation, land cover, population density, etc.)

Random effects model residual variation not explained by covariates

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# LF predictions

###  Maps can help surveillance activities by

### - identifying areas that require enhanced interventions
### - setting a benchmark for evaluating the impact of interventions

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![](./figures/mapsLFb.png)
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# Leptospirosis in Brazil

### Spatio-temporal determinants of urban leptospirosis transmission: Four-year prospective cohort study of slum residents in Brazil

### [Hagan, Moraga, et al., PLOS Neglected Tropical Diseases,<br> 10(1): e0004275, 2016](https://journals.plos.org/plosntds/article?id=10.1371/journal.pntd.0004275)

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# Spatio-temporal determinants of urban leptospirosis transmission in Brazil

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**Transmission** via direct skin or mucosal contact with animal reservoirs or an environment contaminated with infected  urine

**Manifestations**: fever, headache, myalgia, aseptic meningitis, jaundice, renal failure, pulmonary hemorrhage syndrome

Urban health problem due to the **rapid and disorganized expansion of urban centers**, creating ecological conditions for rat-borne transmission

Predominantly affects **vulnerable populations** such as subsistence farmers and residents of urban<br> slums in the tropics

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# Leptospirosis in Pau da Lima, Brazil

Pau da Lima is a urban slum in Salvador, Bahia, that hosts a densely populated community with a high annual incidence of leptospirosis

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# Leptospirosis in Pau da Lima
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### Objective
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We wish to understand the spatio-temporal patterns of leptospirosis and identify targets for intervention in Pau da Lima

### Data
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2003 subjects recruited and followed for a four-year period (2003-2007)

Annual administration of questionnaires and GIS mapping to determine socio-behavioral and environmental risk factors

### Spatio-temporal mixed model
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`$$Y_{ij}| P(\boldsymbol{s}_i, j) \sim Bernoulli(P(\boldsymbol{s}_i, j))$$`

`$$logit(P(\boldsymbol{s}_i, j))=
\boldsymbol{z}_{ij} \boldsymbol{\beta}  + \xi(\boldsymbol{s}_i,j) + u_i$$`

`$$\xi(\boldsymbol{s}_i,j)=a \xi(\boldsymbol{s}_i,j-1)+w(\boldsymbol{s}_i,j)$$`

`$$|a|<1, \xi(\boldsymbol{s}_i,1) \sim N(0, \sigma_w^2/ (1-a^2)),
w(\boldsymbol{s}_i,j) \sim N(0,\sigma^2_w C(h))$$`

`$$u_i \sim N(0, 1/\tau_u)$$`

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# Selection fixed effects

Within each group, fit GAM using all categorical variables, non-linear effects of continuous variables, and  interactions between them

Remove covariates one by one until obtaining a model with minimum AIC

- Demographic and social status factors `$\longrightarrow$` V1
- Health factors `$\longrightarrow$` V2
- Occupational exposures `$\longrightarrow$` V3
- Household environment `$\longrightarrow$` V4
- Household behaviour `$\longrightarrow$` V5
- Household reservoirs `$\longrightarrow$` V6
- Work behaviour `$\longrightarrow$` V7
- Work reservoirs `$\longrightarrow$` V8

Merge groups and explore further simplification by backward elimination until no longer possible to reduce AIC

V1+V2+V3+V4+V5+V6+V7+V8 `$\longrightarrow$` V

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# Selection fixed effects

Model includes:

- year of follow-up (1st, 2nd, 3rd or 4th year)
- gender (male/ female)
- being literate (literate / illiterate)
- house elevation
- observation of rats near house (observe/ not observe)
- contact with mud near house (contact / no contact)
- non-linear effect of age

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# Risk factors

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# Areas with highest (red) and lowest (blue) odds of infection

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<h1>High-risk region</h1> 
<p style="margin-top:-20px; margin-bottom:20px;">High-risk region in which measured risk is consistent with prediction <p>
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<h1>High positive residual</h1> 
<p style="margin-top:-20px; margin-bottom:20px;">Poor household construction and flood risk due to poor infrastructure <p>
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<h1>High negative residual</h1> 
<p style="margin-top:-20px; margin-bottom:20px;">Open sewers with structural barriers to stop overflow <p>
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# Conclusions

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- Young adults, males, and individuals with low social status may have
activities that place them in more frequent or more intense contact
with sources of transmission

- Interventions such providing adequate sanitation systems may reduce
the burden of leptospirosis by protecting residents from contact with
contaminated soil and mud during heavy rain events

- Areas in which transmission risk cannot be adequately explained using
known risk factors enable the identification of new hypotheses about
transmission and guide targeted interventions

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# Join my GeoHealth research group at KAUST!

I am looking for outstanding PhD students and Postdocs to join my group

👩‍💻 Potential research areas include the development of innovative statistical methods and computational tools for health and environmental applications

- Disease mapping, detection of clusters, early detection of outbreaks
- Integration of spatial and spatio-temporal data
- Development of R packages for data analysis and visualization

💪 Work closely with collaborators at KAUST and around the world

✈️ Generous travel funding to attend conferences and workshops

✨ Excellent research environment, free tuition, monthly living allowance, free housing, medical insurance, relocation support

KAUST http://kaust.edu.sa  &nbsp;&nbsp; Statistics Program http://stat.kaust.edu.sa   
Admissions http://admissions.kaust.edu.sa &nbsp;&nbsp; http://studyat.kaust.edu.sa

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# References

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Moraga, P. (2019). *Geospatial Health Data: Modeling and Visualization with R-INLA and Shiny*. Chapman & Hall/CRC Press

Moraga, P., et al. (2019). epiflows: an R package for risk assessment of travel-related spread of disease. *F1000Research*, 7:1374

Moraga, P. (2018). Small Area Disease Risk Estimation and Visualization Using R. *The R Journal*, 10(1):495-506

Moraga, P. (2017). SpatialEpiApp: A Shiny Web Application for the analysis of Spatial and Spatio-Temporal Disease Data. *Spatial and Spatio-temporal Epidemiology*, 23:47-57

Moraga, P., et al. (2017). A geostatistical model for combined analysis of point-level and area-level data using INLA and SPDE. *Spatial Statistics*, 21:27-41

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Moraga, P. and Kulldorff, M. (2016). Detection of spatial variations in temporal trends with a quadratic function. *Statistical Methods for Medical Research*, 25(4):1422-1437

Hagan, J. E., Moraga, P., et al. (2016). Spatio-temporal determinants of urban leptospirosis transmission: Four-year prospective cohort study of slum residents in Brazil. *PLOS Neglected Tropical Diseases*, 10(1): e0004275

Moraga, P., et al. (2015). Modelling the distribution and transmission intensity of lymphatic filariasis in sub-Saharan Africa prior to scaling up interventions: integrated use of geostatistical and mathematical modelling. *Parasites & Vectors*, 8:560

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Thanks!<br>
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<span style = 'font-size: 38px; font-weight:bold'>
Paula Moraga<br>
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<a href='http://twitter.com/Paula_Moraga_' target='_blank'> <i class='fa fa-twitter fa-fw'></i>&nbsp; @Paula_Moraga_</a><br>
<a href='https://Paula-Moraga.github.io/' target='_blank'><i class='fa fa-globe fa-fw'></i>&nbsp; www.PaulaMoraga.com</a><br>
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