**5.3. Bridging the gap between climate information and health services in Colombia and Ecuador**

Climatic factors play a significant role in the transmission dynamics of several infectious diseases [14]. Therefore it should be a critical priority to incorporate climate information into disease risk assessments and early warning and response systems. (See figure 7.) This, in fact, has been one of the goals of the Colombian and Ecuadorian meteorological and health services in recent years. In Colombia, in particular, the National Institute of Health (INS) recently teamed up with the Institute of Hydrology, Meteorology and Environmental Studies (IDEAM) and several universities and research centers to design and implement a proactive, collaborative, multidisciplinary, Integrated Surveillance and Control System – ISCS [15,16]. This initiative is part of a set of measures and policy options that the Colombian adaptation strategy to climate change proposed for three areas of primary concern: the high-altitude Andean ecosystems, the insular and coastal areas, and human health (see Integrated National Adaptation Pilot [16]).

Risk Management at the Latin American Observatory 547

interdisciplinary effort. The implementation of the ISCS has required, among many other activities, analyzing the local eco-epidemiological settings of various malaria- and dengue fever-prone pilot sites, implementing process-based biological and statistical models, and strengthening the local capacity of health authorities. It has also required strengthening the IDEAM capability to routinely and systemically produce and disseminate, relevant, continuous, homogenous, and reliable climate information that could support the decisionmaking process of health authorities. Such information includes ground-truth historical records, modeling simulation outputs, seasonal forecasts, El Niño Southern Oscillation forecasts, and climate change predictions. All this information, along with disease morbidity profiles, entomological conditions, socio-economic drivers, and impacts of interventions and control campaigns, is now steadily becoming a core part of routine activities and disease control plans of health services at regional, municipal and local levels, and is starting to facilitate a better allocation of health resources and more cost-effective preventive responses. On a broader scale, measures proposed as part of the adaptation plan have been mainstreamed into the Colombian political agenda to ensure their sustainability. They have reached, for instance, the 2010-2014 National Development Plan, the 2010-2014 Environmental Action Plan, the Colombia's Poverty Reduction Plan, and the Public Health National Plan.

In Ecuador, in turn, the National Institute of Meteorology and Hydrology (INAMHI) has conducted various research activities [24,25] on the analysis of potential increases in the incidence of malaria and dengue fever in key lowland provinces. Activities have included the implementation and coupled analysis of the 30 km WRF (Weather Research and Forecasting), dynamic downscaling regional climate model, and the Ross-Macdonald malaria infectious disease process-based model, to reproduce the spatio-temporal variability of primary positive cases reported by the National Malaria Eradication Service over a recent 13-year historical period (see figure 8). They have also included a first design of an Early Warning System for malaria and dengue fever outbreaks, which is now developing into an integrated surveillance and climate modeling for malaria and dengue fever predictability in rural and urban settings. The INAMHI has also joined efforts with several research groups to broaden the understanding of the complex transmission dynamics (in both space and time) of these vectorborne diseases, in order to improve decision-making processes in regional and local health authorities and, in a more general sense, to strengthen the already solid surveillance and

control activities of infectious diseases conducted by the Ecuadorian health sector.

Poor air quality conditions can cause many respiratory problems including asthma and severe allergies and can also pose serious health problems such as cancer. In order to issue public warnings of high pollution episodes, the Peruvian National Service of Meteorology and Hydrology – SENAMHI is working on the design and implementation of an air quality forecast system for the cities of Lima and El Callao. The system maps out current and future ambient air quality conditions based on hindcast simulation runs of the BRAMS model and the Weather Research and Forecasting coupled with Chemistry (WRF-Chem) model (see figure 9), as well as on *in situ* measurements of main air pollutants, such as particulate

**5.4. Air quality risk assessment for Lima and El Callao (Peru)** 

**Figure 7.** Climate information and human health integrated surveillance and control systems.

The aim of the adaptation strategy for the health sector is to have a better-prepared institutional arrangement for increased exposure to malaria and dengue fever, two climatesensitive, vector-borne diseases that are still considered human health burdens in Colombia [17]. According to the scientific literature, the potential increase in the incidence of these two diseases is likely to occur not only in the already endemic malaria and dengue fever prone areas, but also on the fringes with the Andean regions where local communities have not been exposed to pathogens before. Thus they lack the immunity against these microorganisms. The approach has been to assist the health sector to better cope with current climate variability and climate-related events, as a means to make it better prepared against future climatic conditions likely to be brought by the ongoing long-term global climate change. As a result, Colombia has been working on reducing people's vulnerabilities to the negative impacts of malaria and dengue outbreaks, as well as developing an Early Warning System framework, supported by seasonal forecasting capabilities, weather and environmental monitoring, and statistical and dynamic models [18-23].

This effort required linking what had previously been two largely separate analytical domains: the field of public health, traditionally dominated by human health experts and practitioners; and weather and climate science, usually lead by meteorologists, climatologists, engineers, and environmental science experts. Several biologists, entomologists and social experts have also joined this collaborative, inter-institutional and interdisciplinary effort. The implementation of the ISCS has required, among many other activities, analyzing the local eco-epidemiological settings of various malaria- and dengue fever-prone pilot sites, implementing process-based biological and statistical models, and strengthening the local capacity of health authorities. It has also required strengthening the IDEAM capability to routinely and systemically produce and disseminate, relevant, continuous, homogenous, and reliable climate information that could support the decisionmaking process of health authorities. Such information includes ground-truth historical records, modeling simulation outputs, seasonal forecasts, El Niño Southern Oscillation forecasts, and climate change predictions. All this information, along with disease morbidity profiles, entomological conditions, socio-economic drivers, and impacts of interventions and control campaigns, is now steadily becoming a core part of routine activities and disease control plans of health services at regional, municipal and local levels, and is starting to facilitate a better allocation of health resources and more cost-effective preventive responses. On a broader scale, measures proposed as part of the adaptation plan have been mainstreamed into the Colombian political agenda to ensure their sustainability. They have reached, for instance, the 2010-2014 National Development Plan, the 2010-2014 Environmental Action Plan, the Colombia's Poverty Reduction Plan, and the Public Health National Plan.

546 Risk Management – Current Issues and Challenges

health (see Integrated National Adaptation Pilot [16]).

WEATHER STATIONS DATA

**CLIMATE INFORMATION**

GENERAL CIRCULATION MODELS DATA

REMOTE SENSING DATA

SEASONAL FORECASTS

MEDIUM- TO LONG-TERM CLIMATE PREDICTIONS

ISCS [15,16]. This initiative is part of a set of measures and policy options that the Colombian adaptation strategy to climate change proposed for three areas of primary concern: the high-altitude Andean ecosystems, the insular and coastal areas, and human

**INFORMATION AND PLATFORMS**

HEALTH SERVICES ROUTINE ACTIVITIES

> RISK ASSESSMENTS

EARLY WARNING AND RESPONSE SYSTEMS

**INTEGRATED SURVEILLANCE AND CONTROL SYSTEMS**

**Figure 7.** Climate information and human health integrated surveillance and control systems.

environmental monitoring, and statistical and dynamic models [18-23].

The aim of the adaptation strategy for the health sector is to have a better-prepared institutional arrangement for increased exposure to malaria and dengue fever, two climatesensitive, vector-borne diseases that are still considered human health burdens in Colombia [17]. According to the scientific literature, the potential increase in the incidence of these two diseases is likely to occur not only in the already endemic malaria and dengue fever prone areas, but also on the fringes with the Andean regions where local communities have not been exposed to pathogens before. Thus they lack the immunity against these microorganisms. The approach has been to assist the health sector to better cope with current climate variability and climate-related events, as a means to make it better prepared against future climatic conditions likely to be brought by the ongoing long-term global climate change. As a result, Colombia has been working on reducing people's vulnerabilities to the negative impacts of malaria and dengue outbreaks, as well as developing an Early Warning System framework, supported by seasonal forecasting capabilities, weather and

**DATA/TOOLS REQUIREMENTS**

This effort required linking what had previously been two largely separate analytical domains: the field of public health, traditionally dominated by human health experts and practitioners; and weather and climate science, usually lead by meteorologists, climatologists, engineers, and environmental science experts. Several biologists, entomologists and social experts have also joined this collaborative, inter-institutional and In Ecuador, in turn, the National Institute of Meteorology and Hydrology (INAMHI) has conducted various research activities [24,25] on the analysis of potential increases in the incidence of malaria and dengue fever in key lowland provinces. Activities have included the implementation and coupled analysis of the 30 km WRF (Weather Research and Forecasting), dynamic downscaling regional climate model, and the Ross-Macdonald malaria infectious disease process-based model, to reproduce the spatio-temporal variability of primary positive cases reported by the National Malaria Eradication Service over a recent 13-year historical period (see figure 8). They have also included a first design of an Early Warning System for malaria and dengue fever outbreaks, which is now developing into an integrated surveillance and climate modeling for malaria and dengue fever predictability in rural and urban settings. The INAMHI has also joined efforts with several research groups to broaden the understanding of the complex transmission dynamics (in both space and time) of these vectorborne diseases, in order to improve decision-making processes in regional and local health authorities and, in a more general sense, to strengthen the already solid surveillance and control activities of infectious diseases conducted by the Ecuadorian health sector.

#### **5.4. Air quality risk assessment for Lima and El Callao (Peru)**

Poor air quality conditions can cause many respiratory problems including asthma and severe allergies and can also pose serious health problems such as cancer. In order to issue public warnings of high pollution episodes, the Peruvian National Service of Meteorology and Hydrology – SENAMHI is working on the design and implementation of an air quality forecast system for the cities of Lima and El Callao. The system maps out current and future ambient air quality conditions based on hindcast simulation runs of the BRAMS model and the Weather Research and Forecasting coupled with Chemistry (WRF-Chem) model (see figure 9), as well as on *in situ* measurements of main air pollutants, such as particulate

Risk Management at the Latin American Observatory 549

12/30/2011 12/31/2011 01/01/2012 01/02/2012

ATE San Borja Campo de Marte

**Date [mm/dd/yyyy]**

concentrations of about 78 and 54 µg/m**3**, respectively.

do not exceed 50 µg/m3.

**Paraguay** 

**Figure 10.** 10 micrometer or less particulate matter (PM10) concentrations (right panel) gathered at the SENAMHI air-quality monitoring stations Ate, San Borja, Campo de Marte, Santa Anita, and Villa Maria del Triunfo (see locations in left panel) over the period spanning from December 30, 2011 through January 2, 2012. The red solid line depicts the health-based daily air quality standard concentration of 150 µg/m**3**. The violet line depicts, in turn, the 250 µg/m**3** daily concentration threshold above which high pollution episode warnings are issued. Air quality is considered 'good' when daily concentrations

**PM10 concentration (µg/m³)**

matter smaller than 10 and 5 micrometers (PM10 and PM5, respectively), nitrogen oxides (NOx) and ground-level ozone (O3). Modeling outputs and real-time information allow the SENAMHI to issue public warnings that could activate municipal plans to help keep pollution

Up to date, the air quality network includes five high-precision monitoring stations located in the surroundings of the aforementioned two densely populated cities. (See figure below.) High pollution episode warnings include events such as the one that took place in the localities of Ate and Villa María del Triunfo on January 1, 2012. *In situ* air quality measurements (figure 10) suggested that PM10 concentrations reached 295 and 290 µg/m**3**, respectively, which exceeded in 45 and 40 µg/m**3** the 250 µg/m3 daily concentration threshold. In the monitoring station Santa Anita, in turn, the PM10 concentration reached 199 µg/m**3**, also exceeding the health-based daily air quality standard concentration of 150 µg/m3. The air quality stations San Borja and Campo de Marte reported moderate daily

**5.5. The agrometeorological bulletin and improved decision-making processes in** 

Extreme weather events such as river floods, severe storms, droughts and below-freezing low temperatures are strongly linked to the onset of El Niño Southern Oscillation (ENSO). Extreme events primarily affect river streamflow and cause numerous direct and indirect impacts on many key sectors of the Paraguayan economy: agriculture, ground and river transportation, potable water, construction, electricity, and recreation. Several studies have demonstrated that the potential impacts of these extreme events and their economic consequences are directly

levels down and alert local residents in Lima districts to the potential health threats.

**Figure 8.** January *Plasmodium vivax* (left panel) and *P. falciparum* (right panel) basic reproductive rates on the Ecuadorian coast, simulated for the period 1996-2008 and for *Anopheles albimanus* mosquito species. (After Muñoz and Recalde [24]).

**Figure 9.** 4-km spatial resolution hindcast WRF-Chem model simulation outputs of NOx concentration fluxes in the geographic domain 12°30'S – 11°30'S and 76°30'W – 77°30'W. Typical NOx concentration fluxes are expressed in thousand mol/km2/hr. The reference arrow represents wind speeds of 4 m/s.

species. (After Muñoz and Recalde [24]).

**Figure 8.** January *Plasmodium vivax* (left panel) and *P. falciparum* (right panel) basic reproductive rates on the Ecuadorian coast, simulated for the period 1996-2008 and for *Anopheles albimanus* mosquito

**Figure 9.** 4-km spatial resolution hindcast WRF-Chem model simulation outputs of NOx concentration fluxes in the geographic domain 12°30'S – 11°30'S and 76°30'W – 77°30'W. Typical NOx concentration fluxes are expressed in thousand mol/km2/hr. The reference arrow represents wind speeds of 4 m/s.

**Figure 10.** 10 micrometer or less particulate matter (PM10) concentrations (right panel) gathered at the SENAMHI air-quality monitoring stations Ate, San Borja, Campo de Marte, Santa Anita, and Villa Maria del Triunfo (see locations in left panel) over the period spanning from December 30, 2011 through January 2, 2012. The red solid line depicts the health-based daily air quality standard concentration of 150 µg/m**3**. The violet line depicts, in turn, the 250 µg/m**3** daily concentration threshold above which high pollution episode warnings are issued. Air quality is considered 'good' when daily concentrations do not exceed 50 µg/m3.

matter smaller than 10 and 5 micrometers (PM10 and PM5, respectively), nitrogen oxides (NOx) and ground-level ozone (O3). Modeling outputs and real-time information allow the SENAMHI to issue public warnings that could activate municipal plans to help keep pollution levels down and alert local residents in Lima districts to the potential health threats.

Up to date, the air quality network includes five high-precision monitoring stations located in the surroundings of the aforementioned two densely populated cities. (See figure below.) High pollution episode warnings include events such as the one that took place in the localities of Ate and Villa María del Triunfo on January 1, 2012. *In situ* air quality measurements (figure 10) suggested that PM10 concentrations reached 295 and 290 µg/m**3**, respectively, which exceeded in 45 and 40 µg/m**3** the 250 µg/m3 daily concentration threshold. In the monitoring station Santa Anita, in turn, the PM10 concentration reached 199 µg/m**3**, also exceeding the health-based daily air quality standard concentration of 150 µg/m3. The air quality stations San Borja and Campo de Marte reported moderate daily concentrations of about 78 and 54 µg/m**3**, respectively.
