Digital surveillance systems for climate-sensitive diseases
Paula Moraga, Ph.D.
Assistant Professor of Statistics
King Abdullah University of Science
and Technology (KAUST), Saudi Arabia
Dengue emergency 2024
Dengue emergency 2024
Dengue
Dengue is a mosquito-borne disease caused by four dengue virus serotypes (DENV1–4) and transmitted by female mosquitoes of the Aedes species
Dengue poses significant public health challenges in tropical and sub-tropical regions of the world, causing considerable health and economic losses
Mosquito-borne diseases
Diseases spread because when a mosquito bites an infected person it also swallows any viruses or parasites living in the blood of the infected person, and these can be transferred to the next person the mosquito bites
Dengue symptoms
Many dengue cases only result in mild, flu-like illness, but some can be severe and even fatal
Dengue virus has four serotypes. Infection with one serotype does not provide immunity against the others, subsequent infections are often more severe
No specific treatment for dengue, usually rest, hydration and pain relievers
Early detection and timely access to proper medical care significantly reduce the fatality rates associated with severe cases
Dengue prevention
Dengue prevention focuses on personal protection using mosquito repellents and long-sleeve shirts, and mosquito control to eliminate breeding sites
(e.g., eliminating standing water, larvicides, insecticides)
Mosquito-borne diseases
Leta et al., International Journal of Infectious Diseases, 2018
What can we do to prevent the spread of infectious diseases?
Need to acknowledge connectivity between people, animals, and their shared environment and work together to prevent disease outbreaks and save lives
Access to healthcare
and education
Vaccine development and mosquito control
Early warning and response systems
Disease surveillance systems
Disease surveillance systems are critical to early detection of epidemics and the design of control strategies
Traditional surveillance systems rely on data gathered with a considerable delay and make surveillance systems ineffective for real-time surveillance
Digital data sources
Real-time digital information may enable to detect outbreaks earlier
“Flu plus fever, not a good way to start the weekend”
“I’m so irritated at this cough and fever”
“This flu, fever & throat ache won’t let me sleep”
Demographic and environmental risk factors
Digital health surveillance system
- Data-gathering platform
- Modelling framework that integrates multiple data sources so as to produce local probabilistic predictions of disease activity
- Interactive dashboard that alerts public health officials when elevated disease levels are anticipated, and provides insights about disease drivers
Dengue surveillance in Brazil
Brazil faced a severe dengue epidemic in 2024
2024 has been the worst year for dengue cases on record, with over 10 million cases reported globally. Brazil has been one of the most affected countries with over 6 million confirmed cases and 6,000 deaths
Dengue epidemic in Brazil
During this time, health systems were overwhelmed, making timely case reporting difficult. As a result, the official case numbers that were being reported in Brazil were underestimating the real number of cases.
This limited the effectiveness of public health decisions
Reporting delays in official dengue cases
In Brazil, the InfoDengue system collects and generates indicators of dengue and other arboviruses:
In principle, dengue is meant to be reported within seven days of case identification. In practice,
- Less than 50% cases are reported within one week
- Less than 75% cases reported within four weeks
- No more than 90% cases reported within nine weeks
Reported dengue cases in Rio de Janeiro, January 2011 to April 2012. Red line reported cases for those weeks.
Black line eventually reported cases after 10 weeks.
Dengue nowcasting by combining official and alternative data sources
We decided to investigate alternative data sources to complement official surveillance data to produce more accurate dengue predictions that help support decision-making
Aim: improving dengue nowcasting in Brazil using real-time search query data
Google Trends data
Google Trends index for a specific keyword is an index ranging from 0 to 100. Calculated using the number of searches for that keyword divided by the total number of searches of the region and time period considered to compare relative popularity
Weekly Google Trends index for keyword ‘dengue’ in Brazil, 2019 to 2024.
Dengue nowcasting in Brazil
We assessed the value of Google Trends for weekly dengue nowcasting in the 27 Brazilian states
Each week from March 2024 to January 2025:
- Download official number of dengue cases reported in InfoDengue and Google Trends indices
- Fit several nowcasting models using different information
- Performance evaluated comparing nowcasts with the actual cases (cases reported after 15 weeks) using error and uncertainty measures
Google Trend index for keyword dengue
Calculated correlation between dengue cases and Google Trends indices for several dengue-related keywords in Brazil, Jan 2013 - Dec 2023
Highest correlation observed between dengue cases and keywords
sintomas dengue (0.93), dengue (0.90), and sintomas de dengue (0.89)
High intercorrelations among keywords. Dengue series less sparse
We decided to use the Google Trend index for keyword dengue
Weekly Google Trends index for keyword ‘dengue’ in Brazil, 2019 to 2024.
Results
Results vary by state. In general, Google Trends and joint model for reported cases and delay distribution by InfoDengue are the best-performing approaches
Weekly dengue nowcasts March to June, 2024
Weekly dengue nowcasts in Rio de Janeiro, March to June, 2024
Dengue tracker in Brazil
Dengue-tracker provides weekly updates on the number of dengue cases per state in Brazil
We present official and corrected case counts incorporating information from Google Trends
Reports assist policymakers
and the general public in understanding dengue levels
and guide their decisions
Dengue tracker in Brazil
Dengue tracker in Brazil
Conclusions
We demonstrated the value of Google Trends data for dengue surveillance during Brazil’s 2024 dengue epidemic
Further research is needed to understand the use of digital data for disease information (e.g., ChatGPT)
Need to understand biases in digital data (not all individuals use search engines, just more educated and younger, internet penetration not same in all regions)
This study highlights the need for multiple, complementary data sources rather than a single data source for disease surveillance, especially during disease outbreaks
Disease forecasting
Nowcasting methods allow us to understand current disease activity levels and make better informed decisions
It is also important to predict the number of cases that will occur in the future so we have more time to be prepared and reduce disease impacts
During the 2024 epidemic, the Brazilian Ministry of Health asked for 2025 dengue predictions to help inform their response and surveillance activities
Disease forecasting
We developed a neural network model for dengue forecasting. The model accounts for complex delayed and non-linear effects of climate variables, and spatial information to obtain improved predictions of future dengue cases
Weekly dengue incidence rate (cases per 100K) in 27 Brazilian states
Climate covariates
Dengue forecasting methods improve their accuracy by including risk factors such as climate and environmental variables known to affect transmission
Borrowing information from neighbors
Neighbors assumed to be regions sharing a common boundary
Goias: Tocantins, Bahia, Minas Gerais, Mato Grosso, Mato Grosso do Sul and Distrito Federal
Model’s performance assessment
We use the first 6 years (i.e., 2016-01-03 to 2021-12-26) to train the model and predict the number of dengue cases 1, 2, 3, 4, 8 and 12 weeks ahead. Then, we move the window one week keeping 6 years for training to predict the number of cases weeks ahead until 2023-12-24
We assessed the model’s performance using error and uncertainty measures in comparison with other approaches that only use cases or climate information
Results
Model proposed performs well overall except northern states. These are regions in the Amazon which are less connected with their neighbors
Performance measures forecasts 4-weeks ahead
| Federal Unit (FU) | Code | LSTM-Cases | LSTM-Climate | LSTM-Climate-Spatial | Bayesian Baseline | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| MAE | MAPE | CRPS | MAE | MAPE | CRPS | MAE | MAPE | CRPS | MAE | MAPE | CRPS | ||
| Acre (AC) | 12 | 305.19 | 45.50% | 90.91 | 129.76 | 22.30% | 35.68 | 136.83 | 24.89% | 37.34 | 382.77 | 47.23% | 96.13 |
| Alagoas (AL) | 27 | 177.96 | 43.29% | 38.14 | 79.24 | 30.54% | 16.27 | 61.08 | 23.17% | 12.98 | 69.39 | 24.28% | 13.41 |
| Amapá (AP) | 16 | 51.21 | 47.90% | 34.05 | 22.45 | 23.49% | 5.35 | 27.45 | 26.98% | 6.02 | 30.53 | 34.09% | 7.12 |
| Amazonas (AM) | 13 | 188.17 | 41.56% | 32.14 | 100.21 | 19.63% | 19.23 | 111.60 | 21.64% | 22.44 | 143.57 | 28.79% | 31.40 |
| Bahia (BA) | 29 | 886.64 | 29.94% | 165.30 | 639.44 | 23.20% | 123.86 | 532.46 | 17.13% | 120.50 | 718.63 | 22.84% | 137.74 |
| Ceará (CE) | 23 | 562.67 | 46.52% | 108.09 | 245.17 | 27.54% | 52.99 | 187.56 | 15.51% | 35.01 | 315.69 | 30.16% | 60.26 |
| Distrito Federal (DF) | 53 | 1040.21 | 26.69% | 244.60 | 926.73 | 23.24% | 219.97 | 767.30 | 16.72% | 211.70 | 997.42 | 24.57% | 249.25 |
| Espírito Santo (ES) | 32 | 8431.94 | 30.90% | 1713.56 | 7262.14 | 30.35% | 1310.95 | 6300.78 | 23.06% | 1308.43 | 6967.74 | 25.93% | 1552.96 |
| Goiás (GO) | 52 | 1708.00 | 30.34% | 310.44 | 1277.24 | 27.36% | 226.00 | 1195.70 | 19.87% | 222.87 | 1722.08 | 29.75% | 321.36 |
| Maranhão (MA) | 21 | 143.59 | 56.31% | 26.44 | 102.87 | 38.07% | 18.93 | 59.27 | 23.91% | 10.88 | 147.31 | 53.05% | 28.14 |
| Mato Grosso (MT) | 51 | 657.81 | 34.65% | 189.42 | 563.40 | 26.97% | 142.56 | 340.72 | 16.69% | 72.73 | 624.21 | 28.36% | 125.27 |
| Mato Grosso do Sul (MS) | 50 | 1711.05 | 75.23% | 342.47 | 568.10 | 59.97% | 108.71 | 404.48 | 40.11% | 81.94 | 1646.17 | 50.03% | 344.61 |
| Minas Gerais (MG) | 31 | 15099.46 | 52.28% | 3253.80 | 7730.47 | 33.33% | 1648.53 | 5088.71 | 24.52% | 1035.86 | 14220.67 | 40.19% | 3472.85 |
| Pará (PA) | 15 | 319.85 | 47.03% | 72.72 | 256.88 | 26.23% | 53.97 | 159.61 | 19.43% | 34.75 | 210.77 | 21.89% | 56.14 |
| Paraná (PR) | 41 | 651.62 | 44.56% | 145.98 | 532.44 | 26.62% | 104.80 | 391.02 | 20.01% | 81.55 | 603.78 | 22.63% | 117.92 |
| Pernambuco (PE) | 26 | 501.76 | 41.95% | 96.53 | 358.33 | 32.90% | 69.27 | 257.65 | 19.53% | 58.65 | 355.72 | 26.02% | 72.60 |
| Piauí (PI) | 22 | 319.75 | 43.87% | 57.96 | 263.10 | 30.81% | 50.76 | 194.54 | 20.75% | 41.58 | 298.91 | 28.17% | 57.16 |
| Rio de Janeiro (RJ) | 33 | 1034.84 | 32.60% | 217.36 | 861.35 | 25.10% | 194.52 | 717.22 | 18.02% | 175.08 | 910.87 | 22.58% | 210.46 |
| Rio Grande do Norte (RN) | 24 | 313.87 | 49.58% | 68.70 | 252.68 | 30.67% | 47.31 | 171.49 | 19.76% | 35.35 | 259.23 | 24.48% | 49.58 |
| Rio Grande do Sul (RS) | 43 | 823.57 | 31.88% | 155.09 | 679.61 | 28.42% | 122.36 | 548.03 | 21.42% | 98.53 | 736.82 | 25.74% | 149.61 |
| Rondônia (RO) | 11 | 371.50 | 79.49% | 103.44 | 300.93 | 42.08% | 79.71 | 285.61 | 40.93% | 69.17 | 323.33 | 43.03% | 104.34 |
| Roraima (RR) | 14 | 9.33 | 40.63% | 6.03 | 6.52 | 43.27% | 5.02 | 6.36 | 44.31% | 5.93 | 8.66 | 52.55% | 2.55 |
| Santa Catarina (SC) | 42 | 7381.10 | 79.16% | 1765.31 | 1585.71 | 56.28% | 395.28 | 1556.58 | 15.21% | 294.09 | 3028.00 | 40.68% | 831.25 |
| São Paulo (SP) | 35 | 9544.39 | 49.61% | 2196.34 | 4088.67 | 31.97% | 921.95 | 3068.46 | 17.28% | 612.34 | 8468.53 | 31.64% | 1961.50 |
| Sergipe (SE) | 28 | 54.35 | 20.82% | 13.07 | 45.92 | 17.48% | 9.43 | 41.52 | 16.50% | 8.11 | 86.08 | 31.38% | 18.57 |
| Tocantins (TO) | 17 | 124.25 | 49.07% | 31.51 | 103.21 | 37.89% | 28.46 | 92.55 | 29.12% | 20.44 | 128.97 | 45.07% | 27.20 |
Mobility
Instead of assuming there was connectivity between adjacent regions, we assumed connectivity if there were people traveling between regions
Consider dataset on mobility spanning air, road, and waterway transport (Oliveira et al. The Lancet Digital Health, 2024)
Spatial modeling including mobility data
Consider contribution of cases imported into each city \(i\) from others in week \(t\): \[ \text{Imported Cases}_{i, t} = \sum_{j \in \mathcal{N}_i} \text{Mobility}_{ji} \cdot \frac{\text{Cases}_{j, t}}{\text{Population}_j} \]
\(\mathcal{N}_i\): set of cities with connections with city \(i\), \(\text{Mobility}_{ji}\): people from city \(j\) to \(i\), \(\text{Cases}_{j, t}\): number cases in city \(j\) and week \(t\), \(\text{Population}_j\) city \(j\)
Results
We assessed the performance of the improved model in selected cities representing different climatic zones and disease dynamics profiles. Results demonstrate the incorporation of human movement patterns improves prediction compared with models that use spatial adjacency structures
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Chen and Moraga, under review, 2025
Conclusions
- Developed a forecasting model that improves accuracy by integrating dengue cases, climate and human mobility
- Model is generalizable and can be applied to forecast other diseases influenced by climate and mobility to improve health surveillance
- Findings in open access papers, and code freely available for reproducibility
Forecasting 2025 dengue cases in Brazil
Brazilian Ministry of Health asked for 2025 dengue predictions to help inform their response and surveillance activities
We participated in the Infodengue-Mosqlimate Dengue Challenge (IMDC) to produce actionable forecasts of the 2025 dengue season
Ensemble forecast for dengue in Brazil, 2025
Predictions for the states of Amazonas (AM), Ceará (CE), Goiás (GO), Paraná (PR), and Minas Gerais (MG)
Dengue forecasting challenge results
The results of the challenge published in September 2024 as a technical report in Portuguese ensuring it reached key decision-makers in Ministry of Health
Dengue forecasting challenge results
- Forecasting methods code and results publicly available on GitHub
- Webinars organized to discuss the challenge and its results that were attended by a large number of public health professionals from Brazil
- We continue our collaboration working on better models to provide improved projections for the 2026 dengue season that help inform prevention and control strategies by the Brazilian Ministry of Health
Spatio-temporal disease prediction integrating compartment and point process models
COVID-19 cases in Cali, Colombia, 2020
SIR model
Fit SIR (Susceptible, Infected, Recovered) compartment model to aggregated data for each time to predict the number of infectious individuals at each time
Number individuals in population \(S(t) + I(t) + R(t) = N\) constant
Disease parameters:
\(\beta > 0\) infectious rate
\(\gamma > 0\) recovery rate
\[\begin{align*} \frac{dS(t)}{dt} &= -\beta S(t) \frac{I(t)}{N} \\ \frac{dI(t)}{dt} &= +\beta S(t) \frac{I(t)}{N} - \gamma I(t) \\ \frac{dR(t)}{dt} &= +\gamma I(t) \end{align*}\]
SIR model could be extended to consider more compartments (e.g.,
\(I_S(t)\) symptomatic, \(I_A(t)\) asymptomatic)
Kermack and McKendrick, Proceedings of the Royal Society, 1927
SIR with age-stratified contact information
We extend the SIR model to allow the incorporation of age-stratified contact information, and the estimation of the spatio-temporal intensity for each population group
Contact matrix with the average number of contacts of individuals with different age groups
Individuals in all age groups tend to mix with others of similar age.
This pattern most pronounced in those aged 5–24 years, least pronounced in those aged 55–69
Children mix with adults 30–39. Middle-aged adults mix with elderly
SIR + LGCP with age contact information
Using simulations and real data, we showed SIR+LGCP model has better performance than LGCP models that do not do use information from the SIR model, especially when making predictions
Spatio-temporal disease intensity
SIR+LGCP model allows us to identify high-risk locations and vulnerable populations to develop better strategies for disease prevention and control
Velocities of disease spread
The spatio-temporal disease intensity obtained can be used to calculate the velocities of disease spread
Directions and magnitudes of the velocities can be mapped at specific times to better examine the spread of the disease throughout the region
Spatial data
In disease surveillance we need to analyze data at different spatial and spatio-temporal resolutions and from different sources
Data fusion
European Environment Agency (EEAA) https://www.eea.europe.eu. NASA Socioeconomic Data and Applications Center (SEDAC) https://sedac.ciesin.columbia.edu
Fast and flexible spatial modeling by assuming a spatially continuous variable underlying all observations modeled using a Gaussian random field
Zhong et al., Journal of the Royal Statistical Society Series A, 2024
Precision disease mapping
Disease mapping is important to understand geographic and temporal patterns of diseases and allocate resources where most needed
Often, maps given at an areal resolution which difficulties decision-making
Map shows malaria prevalence in Mozambique. However, disease risk varies continuously in space & areal data unable to show how risk varies within areas
Areal estimates make difficult targeting health interventions and directing resources where most needed
Disaggregate area-level data
High-resolution estimates permit to find differences in disease risk within study regions, and identify areas and groups of people at higher risk
Open-Source Disease Surveillance System
Team working on dengue surveillance
 Paula Moraga KAUST |
 Xiang Chen KAUST |
 Yang Xiao KAUST |
 Guilherme Soares University of São Paulo |
 Rafael Izbicki Federal University of São Carlos |
 Leo Bastos Fiocruz |
Visit Fiocruz and InfoDengue group, Brazil
Visit Ministry of Health and University, Malaysia
Visit Ministry of Health and University, Malaysia
Capacity Building
Courses equip researchers on methods and tools to quantify disease burden, understand geographic and temporal patterns, identify risk factors, and measure inequalities
They also show how to easily turn analyses into visually informative and interactive reports and dashboards that facilitate the communication of insights to collaborators and policymakers
Books
Conclusions
Data crucial for public health decision-making
We need data for public health decision-making. But not just any data
We need reliable, relevant, timely and detailed data to understand how different populations and regions are doing and be able to take efficient and effective actions to reduce disease burden and protect all populations
Collaborative research, data, and analytical tools crucial for solving health challenges, achieving sustainable development, and leaving no one behind
Thank you!
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