class: center, middle, inverse, title-slide

# Mapping Trypanasoma cruzi Infection Prevalence
### Andreas Bender (@adibender)
### Nick Golding, Andre Python, Catherine Moyes
### 2019/03/18

---

# *Trypanosoma cruzi* and Chagas
American trypanosomiasis, or **Chagas disease is a leading cause of heart disease in Latin America**
- It is **one of the 10 neglected diseases addressed by the London Declaration**
which calls for **control and elimination of these devastating diseases by 2020**, based on the World Health Organization (WHO) Road map for overcoming the global impact of neglected tropical diseases.

- Caused by infection with the *Trypanosoma cruzi* parasite

- Transmitted to humans by triatomine vectors (100 species from 18 different genera)

.boxed_grey[
The primary aim of this project is to **map the prevalence of** ***T. cruzi***
**infections in vector species**, in order to investigate spatial heterogeneity
in vectorial transmission, to **target vector control**, and to
**quantify the population at risk**.
]

---
layout: true
class: split-three
.column.bg-main1[.content.vmiddle[
 
#### 1. Species distribution
<img src="figures/p_presence_raw.png">

- `$\sim$` 50 different species
- presence only data
- preferential sampling
]]
.column.bg-main2[.content[
 &nbsp;

#### 2. Infection prevalence in vectors
<img class="center", src="figures/p-spat-vect-inf-prev-1.png">
- point- and polygon-level data
- spatial sparseness

]]
.column.bg-main3[.content.vmiddle[
 
#### 3. Infection prevalence in humans
.img.fill[![](figures/p_hiprev_raw.png)]
- additional data set of acute cases
]]

# Data base

---
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layout: true
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# Road map

$$
`\begin{equation}
\eta(s) = \sum_{p=1}^{P} f_{p}(x_p(s)) + GP(s);\ p(s) = (1-\exp(\eta(s)))^{-1}
\end{equation}`
$$

.row.bg-main1[.content[
 &nbsp; &nbsp; &nbsp; &nbsp;
#### 1. Species distribution
- `$y(s) \in \{0,1\} \sim Bernoulli(p(s))$`
- **Target-group background** approach for sampling bias correction
- Stacked Generalization for combination of different methods (GAM, XGB, RF, ...)

]]
.row.bg-main2[.content[
 &nbsp;

#### 2. Infection prevalence in vectors
- `$N_{positive}(s) \sim Binomial(N_{tested}(s), p(s))$`
- SDMs for **data augmentation** (combining point-level and
polygon-level observations)
- Model evaluation based on **spatial block-wise cross-validation** to improve
transferability
]]
.row.bg-main3[.content[

#### 3. Infection prevalence in humans
- `$N_{positive}(s) \sim Binomial(N_{tested}(s), p(s))$`
- Additional **uncertainty w.r.t. location of infection** 
( `$\rightarrow$` children under-10; integration of "acute cases" data base)
]]

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---
layout: true
class: split-three
.column.bg-main1[.content.vmiddle[
 
#### 1. Species distribution
.center[
<img class = "center" src="figures/p-pans-meg-gam.png" height="350">
]

]]

.column.bg-main2[.content[
 &nbsp; 
#### 2. Infection prevalence in vectors
<img class="center", src="figures/iprev-vectors.png">

]]
.column.bg-main3[.content.vmiddle[
 
#### 3. Infection prevalence in humans
<img src="figures/page_blank.jpeg">
]]

# Current progress

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# Open Questions and Discussion

- How much does outdoor biting contribute to transmission?

- How much does contamination of food/drink by vectors contribute to transmission?

- How long after the elimination of vectors from homes (through insecticide spraying) will re-infestation occur?

- Does insecticide resistance have the potential to derail the elimination of vectors from homes?

- Does the effect of improved housing change if all houses are improved, compared to the situation where there is a mosaic of poor-quality and higher-quality housing?

---
layout: false
# References
.font80[
<a name=bib-bhatt_improved_2017></a>[Bhatt, S, E. Cameron, S. R.
Flaxman, et al.](#cite-bhatt_improved_2017) (2017). "Improved
prediction accuracy for disease risk mapping using Gaussian
process stacked generalization". En. In: _Journal of The Royal
Society Interface_ 14.134, p. 20170520. DOI:
[10.1098/rsif.2017.0520](https://doi.org/10.1098/rsif.2017.0520).

<a name=bib-browne_contemporary_2017></a>[Browne, A. J, C. A.
Guerra, R. V. Alves, et al.](#cite-browne_contemporary_2017)
(2017). "The contemporary distribution of Trypanosoma cruzi
infection in humans, alternative hosts and vectors". En. In:
_Scientific Data_ 4, p. 170050. DOI:
[10.1038/sdata.2017.50](https://doi.org/10.1038/sdata.2017.50).

<a name=bib-ceccarelli_datatri_2018></a>[Ceccarelli, S, A.
Balsalobre, P. Medone, et al.](#cite-ceccarelli_datatri_2018)
(2018). "DataTri, a database of American triatomine species
occurrence". En. In: _Scientific Data_ 5, p. 180071. DOI:
[10.1038/sdata.2018.71](https://doi.org/10.1038/sdata.2018.71).

<a name=bib-miller_recent_2019></a>[Miller, D. A. W, K. Pacifici,
J. S. Sanderlin, et al.](#cite-miller_recent_2019) (2019). "The
recent past and promising future for data integration methods to
estimate species’ distributions". En. In: _Methods in Ecology and
Evolution_ 10.1, pp. 22-37. DOI:
[10.1111/2041-210X.13110](https://doi.org/10.1111/2041-210X.13110).

<a name=bib-phillips_sample_2009></a>[Phillips, S. J, M. Dudík, J.
Elith, et al.](#cite-phillips_sample_2009) (2009). "Sample
selection bias and presence-only distribution models: implications
for background and pseudo-absence data". En. In: _Ecological
Applications_ 19.1, pp. 181-197. DOI:
[10.1890/07-2153.1](https://doi.org/10.1890/07-2153.1).

<a name=bib-roberts_cross-validation_2017></a>[Roberts, D. R, V.
Bahn, S. Ciuti, et al.](#cite-roberts_cross-validation_2017)
(2017). "Cross-validation strategies for data with temporal,
spatial, hierarchical, or phylogenetic structure". En. In:
_Ecography_ 40.8, pp. 913-929. DOI:
[10.1111/ecog.02881](https://doi.org/10.1111/ecog.02881).

<a name=bib-valavi_blockcv_2018></a>[Valavi, R, J. Elith, J. J.
Lahoz-Monfort, et al.](#cite-valavi_blockcv_2018) (2018).
"blockCV: An r package for generating spatially or environmentally
separated folds for k-fold cross-validation of species
distribution models". In: _Methods in Ecology and Evolution_ 0.0.
DOI:
[10.1111/2041-210X.13107](https://doi.org/10.1111/2041-210X.13107).
]

---
class: inverse center middle
# Appendix
<html>
 <div style='float:left'></div>
 <hr color='#EB811B' size=1px width=720px>
</html>

---
# Target-group background approach
<img src="figures/block-generation-sdm-1.png">

1. Convex hull around species presence locations

2. Extended convex hull

3. Define absences as reported presence locations of all other species

4. Set up spatial block-wise cross-validation (each fold 1-5 `$\sim$` same
  proportion of presences and absences)

---
# GAM vs. XGB
.center[
<img src="figures/p-pans-meg-gam-xgb.png" height="500px">
]

Notes for current slide

Notes for next slide

Mapping Trypanasoma cruzi Infection PrevalenceAndreas Bender (@adibender)Nick Golding, Andre Python, Catherine Moyes2019/03/181

Trypanosoma cruzi and Chagas

American trypanosomiasis, or Chagas disease is a leading cause of heart disease in Latin America

It is one of the 10 neglected diseases addressed by the London Declaration which calls for control and elimination of these devastating diseases by 2020, based on the World Health Organization (WHO) Road map for overcoming the global impact of neglected tropical diseases.
Caused by infection with the Trypanosoma cruzi parasite
Transmitted to humans by triatomine vectors (100 species from 18 different genera)

The primary aim of this project is to map the prevalence of T. cruzi infections in vector species, in order to investigate spatial heterogeneity in vectorial transmission, to target vector control, and to quantify the population at risk.

1. Species distribution

$\sim$ 50 different species
presence only data
preferential sampling

2. Infection prevalence in vectors

point- and
polygon-level data
spatial sparseness

3. Infection prevalence in humans

additional data set of
acute cases

Data base

1. Species distribution

$\sim$ 50 different species
presence only data
preferential sampling

2. Infection prevalence in vectors

point- and
polygon-level data
spatial sparseness

3. Infection prevalence in humans

additional data set of
acute cases

Data base

1. Species distribution

$\sim$ 50 different species
presence only data
preferential sampling

2. Infection prevalence in vectors

point- and
polygon-level data
spatial sparseness

3. Infection prevalence in humans

additional data set of
acute cases

Data base

Road map

$η (s) = \sum_{p = 1}^{P} f_{p} (x_{p} (s)) + G P (s); p (s) = (1 - \exp (η (s)))^{- 1}$

1. Species distribution

$y (s) \in {0, 1} \sim B e r n o u l l i (p (s))$
Target-group background approach for sampling bias correction
Stacked Generalization for combination of different methods (GAM, XGB, RF, ...)

2. Infection prevalence in vectors

$N_{p o s i t i v e} (s) \sim B i n o m i a l (N_{t e s t e d} (s), p (s))$
SDMs for data augmentation (combining point-level and polygon-level observations)
Model evaluation based on spatial block-wise cross-validation to improve transferability

3. Infection prevalence in humans

$N_{p o s i t i v e} (s) \sim B i n o m i a l (N_{t e s t e d} (s), p (s))$
Additional uncertainty w.r.t. location of infection
( $\to$ children under-10; integration of "acute cases" data base)

Road map

$η (s) = \sum_{p = 1}^{P} f_{p} (x_{p} (s)) + G P (s); p (s) = (1 - \exp (η (s)))^{- 1}$

1. Species distribution

$y (s) \in {0, 1} \sim B e r n o u l l i (p (s))$
Target-group background approach for sampling bias correction
Stacked Generalization for combination of different methods (GAM, XGB, RF, ...)

2. Infection prevalence in vectors

$N_{p o s i t i v e} (s) \sim B i n o m i a l (N_{t e s t e d} (s), p (s))$
SDMs for data augmentation (combining point-level and polygon-level observations)
Model evaluation based on spatial block-wise cross-validation to improve transferability

3. Infection prevalence in humans

$N_{p o s i t i v e} (s) \sim B i n o m i a l (N_{t e s t e d} (s), p (s))$
Additional uncertainty w.r.t. location of infection
( $\to$ children under-10; integration of "acute cases" data base)

Road map

$η (s) = \sum_{p = 1}^{P} f_{p} (x_{p} (s)) + G P (s); p (s) = (1 - \exp (η (s)))^{- 1}$

1. Species distribution

$y (s) \in {0, 1} \sim B e r n o u l l i (p (s))$
Target-group background approach for sampling bias correction
Stacked Generalization for combination of different methods (GAM, XGB, RF, ...)

2. Infection prevalence in vectors

$N_{p o s i t i v e} (s) \sim B i n o m i a l (N_{t e s t e d} (s), p (s))$
SDMs for data augmentation (combining point-level and polygon-level observations)
Model evaluation based on spatial block-wise cross-validation to improve transferability

3. Infection prevalence in humans

$N_{p o s i t i v e} (s) \sim B i n o m i a l (N_{t e s t e d} (s), p (s))$
Additional uncertainty w.r.t. location of infection
( $\to$ children under-10; integration of "acute cases" data base)

1. Species distribution

2. Infection prevalence in vectors

3. Infection prevalence in humans

Current progress

1. Species distribution

2. Infection prevalence in vectors

3. Infection prevalence in humans

Current progress

1. Species distribution

2. Infection prevalence in vectors

3. Infection prevalence in humans

Current progress

Open Questions and Discussion

How much does outdoor biting contribute to transmission?
How much does contamination of food/drink by vectors contribute to transmission?
How long after the elimination of vectors from homes (through insecticide spraying) will re-infestation occur?
Does insecticide resistance have the potential to derail the elimination of vectors from homes?
Does the effect of improved housing change if all houses are improved, compared to the situation where there is a mosaic of poor-quality and higher-quality housing?

References

Bhatt, S, E. Cameron, S. R. Flaxman, et al. (2017). "Improved prediction accuracy for disease risk mapping using Gaussian process stacked generalization". En. In: Journal of The Royal Society Interface 14.134, p. 20170520. DOI: 10.1098/rsif.2017.0520.

Browne, A. J, C. A. Guerra, R. V. Alves, et al. (2017). "The contemporary distribution of Trypanosoma cruzi infection in humans, alternative hosts and vectors". En. In: Scientific Data 4, p. 170050. DOI: 10.1038/sdata.2017.50.

Ceccarelli, S, A. Balsalobre, P. Medone, et al. (2018). "DataTri, a database of American triatomine species occurrence". En. In: Scientific Data 5, p. 180071. DOI: 10.1038/sdata.2018.71.

Miller, D. A. W, K. Pacifici, J. S. Sanderlin, et al. (2019). "The recent past and promising future for data integration methods to estimate species’ distributions". En. In: Methods in Ecology and Evolution 10.1, pp. 22-37. DOI: 10.1111/2041-210X.13110.

Phillips, S. J, M. Dudík, J. Elith, et al. (2009). "Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data". En. In: Ecological Applications 19.1, pp. 181-197. DOI: 10.1890/07-2153.1.

Roberts, D. R, V. Bahn, S. Ciuti, et al. (2017). "Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure". En. In: Ecography 40.8, pp. 913-929. DOI: 10.1111/ecog.02881.

Valavi, R, J. Elith, J. J. Lahoz-Monfort, et al. (2018). "blockCV: An r package for generating spatially or environmentally separated folds for k-fold cross-validation of species distribution models". In: Methods in Ecology and Evolution 0.0. DOI: 10.1111/2041-210X.13107.

Appendix
8

Target-group background approach

Convex hull around species presence locations
Extended convex hull
Define absences as reported presence locations of all other species
Set up spatial block-wise cross-validation (each fold 1-5 $\sim$ same proportion of presences and absences)

GAM vs. XGB

Trypanosoma cruzi and Chagas

American trypanosomiasis, or Chagas disease is a leading cause of heart disease in Latin America

It is one of the 10 neglected diseases addressed by the London Declaration which calls for control and elimination of these devastating diseases by 2020, based on the World Health Organization (WHO) Road map for overcoming the global impact of neglected tropical diseases.
Caused by infection with the Trypanosoma cruzi parasite
Transmitted to humans by triatomine vectors (100 species from 18 different genera)

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