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🌡️ Malaria-Climate Link: Understanding the Connection

Welcome to the foundational theory behind climate-driven malaria modeling! This lecture explores how climate variables shape malaria transmission dynamics and why models like VECTRI are essential for forecasting and intervention planning.

🎯 Learning Objectives

By the end of this lecture, you will be able to:

What You'll Master

🌍 Climate-Biology Interactions

  • Understand how climate variables (temperature, rainfall, humidity, and hydrology) control mosquito vector and Plasmodium parasite life cycles
  • Identify critical thresholds and optimal conditions for development and survival

📊 Epidemiological Metrics

  • Grasp key rates and probabilities:
    • Development rates (larval, gonotrophic, sporogonic)
    • Survival probabilities (larvae, adults)
    • Biting rates and host interactions
    • Entomological Inoculation Rate (EIR)
    • Transmission probabilities (vector ↔ host)
  • Understand how these metrics respond dynamically to climate

🖥️ Modeling Framework

  • Recognize the role of models like VECTRI in integrating climate data
  • Understand nonlinear responses and spatial dynamics in transmission forecasting

📚 Lecture Outline

This lecture is organized into six interconnected modules:

  • 1. Climate-Sensitive Transmission System

    Explore the fundamental relationships between climate and malaria transmission

    Jump to Section

  • 2. Vector Life Cycle

    Understand mosquito development from larvae to adults and climate dependencies

    Jump to Section

  • 3. Parasite Development

    Learn how Plasmodium parasites develop inside mosquito vectors

    Jump to Section

  • 4. Host Dynamics

    Examine human population factors affecting transmission intensity

    Jump to Section

  • 5. Environmental Factors

    Discover how hydrology creates and sustains breeding habitats

    Jump to Section

  • 6. Modeling Approaches

    Introduction to dynamical models for malaria transmission

    Jump to Section

1. Malaria as a Climate-Sensitive Transmission System

1.1 Climate and Health Relationship

Malaria is one of the most climate-sensitive diseases on Earth. Understanding this relationship is crucial for predicting transmission patterns and planning interventions.

Core Concept

Malaria transmission is a complex system where climate factors simultaneously affect:

  • Vectors (mosquitoes): development, survival, behavior
  • Parasites (Plasmodium): maturation rates, infectivity
  • Humans: exposure patterns, immunity dynamics

The transmission cycle requires all three components to align within favorable climate conditions. A disruption in any component can halt transmission entirely.

🌡️ Climate Drivers of Malaria

Climate affects malaria transmission through multiple pathways:

Impact on Biology

  • Larval Development: Higher temperatures accelerate development (within limits)

    • Minimum threshold: ~16°C for most Anopheles species
    • Optimal range: 25-28°C
    • Lethal threshold: >37°C

  • Adult Survival: Bell-shaped relationship

    • Too cold: metabolic shutdown
    • Optimal: 20-25°C
    • Too hot: desiccation and heat stress

  • Parasite Development (EIP): Exponentially sensitive

    • Below 16°C: parasites cannot complete development
    • At 20°C: EIP ≈ 23 days
    • At 30°C: EIP ≈ 9 days

Critical Threshold

For transmission to occur, mosquitoes must survive longer than the EIP. This creates a temperature-dependent transmission threshold around 18-20°C.

Impact on Breeding Habitat

  • Breeding Site Creation: Rain creates temporary pools

    • Light rain (1-10 mm/day): maintains existing sites
    • Heavy rain (>30 mm/day): creates new sites but flushes larvae

  • Habitat Persistence: Balance between inflow and evaporation

    • Dry periods: breeding sites dry up → population collapse
    • Wet periods: abundant habitat → population expansion

  • Seasonal Patterns: Transmission often peaks 1-2 months after peak rainfall

Regional Variation

  • Sahel: Strong seasonal transmission follows monsoon rains
  • Tropics: Year-round transmission with wet season peaks
  • Highlands: Transmission limited to warm, wet periods

Impact on Adult Survival

  • Desiccation Risk: Low humidity shortens mosquito lifespan

    • <40% RH: severe survival penalty
    • 60-80% RH: optimal

    • 95% RH: little additional benefit

  • Flight Behavior: Humidity affects host-seeking

    • High humidity: increased flight activity
    • Low humidity: mosquitoes shelter, reducing biting

  • Indoor vs Outdoor: Houses often provide more stable humidity

Practical Implication

Desert-edge regions may have low transmission despite adequate rainfall due to low humidity limiting mosquito survival.

Impact on Breeding Sites

  • Temporary Pools: Created by rainfall, evaporation, infiltration

    • Duration: 1-3 weeks typical
    • Productivity: high (fewer predators)

  • Permanent Water Bodies: Rivers, lakes, wetlands

    • Duration: year-round
    • Productivity: lower (more predators, competition)

  • Irrigation Systems: Human-managed water

    • Can create stable breeding habitat
    • Extends transmission seasons

Development Impact

Irrigation and dam projects can dramatically increase malaria risk by creating stable breeding habitats year-round.

📈 Typical Climate Sensitivities of Vector-Borne Diseases (VBDs)

Vector-borne diseases show nonlinear responses to climate variables. Small changes near critical thresholds can produce large changes in transmission.

Key Nonlinearities

1. Temperature Thresholds

  • Lower threshold: Below this, development cannot occur
  • Optimal range: Rapid development, high transmission
  • Upper threshold: Above this, mortality increases rapidly

Example: A shift from 18°C to 20°C can enable transmission. A shift from 25°C to 27°C may triple transmission intensity.

2. Rainfall Thresholds

  • Minimum: Insufficient breeding habitat
  • Optimal: Maximum habitat without excessive flushing
  • Maximum: Heavy flushing reduces larval survival

Example: 50 mm/month may produce little transmission. 150 mm/month may be highly favorable. 400 mm/month may reduce transmission due to flushing.

3. Seasonal Amplification

  • Transmission is concentrated when multiple factors are simultaneously favorable
  • Even short favorable periods can produce epidemics

Example: A single month of optimal conditions can sustain transmission for 2-3 months due to population momentum.

🔗 Non-Climate Interactions

Climate doesn't act in isolation. Its impact on malaria is modulated by human and environmental factors:

How It Modifies Climate Impact

  • Deforestation:

    • Increases temperature (loss of shade)
    • Creates sun-lit pools (favorable for An. gambiae)
    • Reduces humidity

  • Urbanization:

    • Urban heat island effect (higher temperatures)
    • Reduces breeding sites (paved surfaces)
    • Changes mosquito species composition

  • Agriculture:

    • Irrigation creates stable breeding sites
    • Rice paddies particularly favorable
    • Can override seasonal rainfall patterns

How It Affects Transmission

  • Low Density (<10 people/km²):

    • Mosquitoes feed on animals (zoophily)
    • Low human biting rate
    • Low transmission despite high vector density

  • Intermediate Density (10-500 people/km²):

    • Transition to human feeding (anthropophily)
    • Maximum per-person biting rate
    • Highest transmission risk

  • High Density (>500 people/km²):

    • Vector abundance limited
    • Per-person biting rate decreases
    • Transmission may decline

How They Interact with Climate

  • Insecticide-Treated Nets (ITNs):

    • Reduce biting rates regardless of climate
    • Effectiveness may vary with mosquito behavior
    • Climate affects compliance (too hot → net usage drops)

  • Indoor Residual Spraying (IRS):

    • Targets indoor-resting mosquitoes
    • Effectiveness depends on mosquito behavior
    • Climate affects spray persistence

  • Larval Source Management:

    • Reduces breeding sites
    • Effectiveness depends on hydrology
    • Rain can overwhelm control efforts

Climate-Intervention Feedback

Climate change may shift mosquito behavior, potentially reducing effectiveness of indoor-targeted interventions like ITNs and IRS.

1.2 Vector Life Cycle Components

The mosquito vector undergoes several distinct life stages, each with unique climate sensitivities.

🥚 Larval Cycle

The aquatic stages (egg → larvae → pupae → adult) are highly temperature-dependent.

Development Stages

1. Egg Stage (1-3 days)

  • Eggs laid on water surface
  • Hatch within 2-3 days at 25°C
  • Can survive brief dry periods (some species)

2. Larval Stage (5-14 days at 25-30°C)

  • Four instars (L1 → L2 → L3 → L4)
  • Feed on microorganisms
  • Most vulnerable to predation and environmental stress
  • Temperature-driven development:
\[ R_L = \frac{T_{wat} - T_{L,min}}{K_L} \]

Where:

  • \(R_L\) = development rate (fraction per day)
  • \(T_{wat}\) = water temperature (°C)
  • \(T_{L,min}\) = minimum threshold (typically 16°C)
  • \(K_L\) = degree-days required (typically 90-100)

3. Pupal Stage (1-2 days)

  • Non-feeding stage
  • Rapid metamorphosis to adult
  • Less vulnerable than larvae

Climate Sensitivity

Temperature Development Time Notes
16°C No development Below threshold
20°C ~18 days Slow development
25°C ~10 days Optimal
30°C ~6 days Fast but risky
35°C ~4 days High mortality
>37°C Lethal Complete mortality

☠️ Larval Mortality

Larvae face multiple mortality sources, many climate-related:

Natural Enemies

  • Fish (especially Gambusia)
  • Aquatic insects (dragonfly nymphs, beetles)
  • Tadpoles and frogs
  • Copepods (small crustaceans)

Climate Connection:

  • Temporary pools (rain-created) → fewer predators
  • Permanent water → more predators
  • Temperature affects predator activity

Density-Dependent Mortality

  • Larvae compete for food
  • Waste products accumulate
  • Survival decreases as density increases

Mathematical Representation:

\[ P_{L,surv} = P_{L,surv0} \times \left(1 - \frac{M_L}{w \times M_{L,max}}\right) \]

Where:

  • \(M_L\) = larval biomass (mg/m²)
  • \(w\) = pond coverage fraction
  • \(M_{L,max}\) = carrying capacity

Climate Connection:

  • Rain creates habitat → reduces crowding
  • Evaporation reduces habitat → increases crowding

Washout Effects

  • Heavy rain washes larvae out of pools
  • Early instars (L1, L2) most vulnerable
  • Can cause sudden population crashes

Flushing Function:

\[ K_{flush} = L_f + (1 - L_f) \times \left[(1 - K_{\infty}) e^{-R_d/\tau} + K_{\infty}\right] \]

Where:

  • \(L_f\) = larval stage (0=early, 1=late)
  • \(R_d\) = daily rainfall (mm)
  • \(\tau\) = flushing scale (~50 mm/day)
  • \(K_{\infty}\) = survival under heavy rain (~0.4)

Implications:

  • Moderate rain: beneficial (creates habitat)
  • Heavy rain: detrimental (flushes larvae)
  • Optimal: 5-15 mm/day with occasional heavier events

Direct Temperature Effects

  • Too Cold (<16°C): development halts, starvation
  • Too Hot (>34°C): metabolic stress, hypoxia

  • Lethal (>37°C): protein denaturation, death

  • Fluctuations: Rapid changes more stressful than gradual

Climate Change Concern:

  • More frequent heat waves → episodic die-offs
  • Warmer minimum temperatures → extended seasons

🩸 Gonotrophic Cycle

The gonotrophic cycle is the time between blood meals and egg laying in adult female mosquitoes.

Cycle Stages

1. Host Seeking (varies with hunger, temperature)

  • Peak activity: dusk and dawn
  • Lasts minutes to hours

2. Blood Feeding (few minutes)

  • Female mosquitoes only
  • Required for egg development
  • Risk of parasite acquisition

3. Blood Digestion & Egg Maturation (2-4 days at 25°C)

  • Temperature-dependent rate:
\[ R_{gono} = \frac{T_{eff} - T_{gono,min}}{K_{gono}} \]
  • \(T_{gono,min}\) ≈ 7.7°C
  • \(K_{gono}\) ≈ 37 degree-days

4. Oviposition (egg laying)

  • Female seeks water body
  • Lays 50-200 eggs
  • Cycle repeats

Temperature Impact on Gonotrophic Period

Temperature Gonotrophic Period Biting Frequency
15°C ~5 days Low
20°C ~3 days Moderate
25°C ~2 days High
30°C ~1.5 days Very High

Transmission Implication

Shorter gonotrophic cycles mean more frequent blood meals → higher transmission rates. Warmer temperatures increase biting pressure.

💀 Vector Survival

Adult mosquito daily survival probability is critical for transmission because parasites require 8-30 days to develop inside the mosquito (EIP).

Martens II Survival Model

\[ P_{V,surv} = \exp\left(-\frac{1}{K_0 + K_1 T_{eff} + K_2 T_{eff}^2}\right) \]

Where:

  • \(K_0\) ≈ -4.4
  • \(K_1\) ≈ 1.31
  • \(K_2\) ≈ -0.03

This produces a bell-shaped relationship between temperature and survival.

Expected Lifespan

\[ \text{Lifespan} = \frac{1}{1 - P_{V,surv}} \text{ days} \]
Temperature Daily Survival Expected Lifespan
15°C 0.70 ~3 days
20°C 0.88 ~8 days
25°C 0.92 ~13 days
28°C 0.91 ~11 days
32°C 0.85 ~7 days
35°C 0.75 ~4 days

Transmission Threshold

For malaria transmission: Lifespan > EIP

  • At 20°C: EIP ≈ 23 days, Lifespan ≈ 8 days → No transmission
  • At 25°C: EIP ≈ 12 days, Lifespan ≈ 13 days → Marginal transmission
  • At 28°C: EIP ≈ 9 days, Lifespan ≈ 11 days → Efficient transmission

1.3 Parasite Development

🦠 Sporogonic Cycle

The sporogonic cycle is the development of Plasmodium parasites inside the mosquito vector.

Parasite Journey

1. Gametocyte Ingestion (during blood meal)

  • Mosquito ingests male and female gametocytes from human blood

2. Fertilization (in mosquito gut, <1 day)

  • Gametocytes mature to gametes
  • Fertilization produces zygote

3. Ookinete Formation (1-2 days)

  • Zygote develops into motile ookinete
  • Penetrates gut wall

4. Oocyst Development (7-14 days)

  • Forms on outer gut wall
  • Produces thousands of sporozoites
  • Most temperature-sensitive stage

5. Sporozoite Migration (1-2 days)

  • Oocyst ruptures
  • Sporozoites migrate to salivary glands
  • Mosquito now infectious

Temperature-Dependent Development

\[ R_{sporo} = \frac{T_{eff} - T_{sporo,min}}{K_{sporo}} \]

Where:

  • \(T_{sporo,min}\) ≈ 16°C (critical threshold)
  • \(K_{sporo}\) ≈ 111 degree-days

Extrinsic Incubation Period (EIP)

\[ \text{EIP} = \frac{1}{R_{sporo}} = \frac{K_{sporo}}{T_{eff} - T_{sporo,min}} \text{ days} \]
Temperature EIP Duration Transmission Potential
<16°C Infinite No transmission
18°C ~56 days Very low
20°C ~28 days Low
25°C ~12 days Moderate
28°C ~9 days High
30°C ~8 days Very high

Critical Temperature Threshold

Below 16°C: Parasites cannot complete development, regardless of time. This creates an absolute lower temperature limit for malaria transmission.

1.4 Host Dynamics

Human populations are not passive receivers of malaria; they are active components of the transmission system.

👥 Host Community

Population Density Effects

The relationship between human population density and malaria risk is non-monotonic (not always increasing).

Zoophily to Anthropophily Transition

Low Density (sparse rural, <10 people/km²)

  • Mosquitoes preferentially feed on animals (cattle, goats)
  • Zoophilic behavior dominant
  • Low human biting rate despite high vector abundance
  • Result: Low malaria risk

Intermediate Density (rural villages, 10-500 people/km²)

  • Sufficient humans to attract mosquitoes
  • Livestock often present but humans preferred
  • Anthropophilic behavior increases
  • Maximum per-capita biting rate
  • Result: Highest malaria risk (per person)

High Density (towns/cities, >500 people/km²)

  • Breeding sites scarce (less habitat)
  • Vectors per person decreases
  • Biting rate per person declines
  • Result: Lower malaria risk (per person)

Human Biting Rate (HBR)

\[ \text{HBR} = \frac{(1 - e^{-H/\tau_{zoo}}) \times V_{biting}}{H} \]

Where:

  • \(H\) = human population density
  • \(\tau_{zoo}\) = zoophily scale (~50 people/km²)
  • \(V_{biting}\) = biting mosquito density

Entomological Inoculation Rate (EIR)

\[ \text{EIR} = \text{HBR} \times \text{CSPR} \]

Where CSPR = Circumsporozoite Protein Rate (fraction of infectious mosquitoes)

Regional Patterns

  • High EIR (>100 infectious bites/person/year): Stable, endemic transmission
  • Low EIR (<10): Unstable, epidemic-prone transmission
  • Climate modulates EIR by affecting vector density and CSPR

🛡️ Immunity

Malaria immunity is complex, partial, and short-lived.

Types of Immunity

1. Anti-Parasite Immunity

  • Reduces parasite density
  • Develops slowly (years of exposure)
  • Never complete (can still be infected)

2. Anti-Disease Immunity

  • Prevents severe symptoms
  • Develops faster than anti-parasite immunity
  • Adults in endemic areas often asymptomatic

3. Anti-Transmission Immunity

  • Reduces gametocyte production
  • Limits mosquito infection
  • Least understood component

Immunity Dynamics

  • Acquisition: Proportional to EIR (more exposure → faster acquisition)
  • Loss: Decays with time (~1-3 years without exposure)
  • Age Patterns:
  • Children: Low immunity → clinical malaria
  • Adults (endemic areas): Partial immunity → asymptomatic infections
  • Adults (epidemic areas): Low immunity → severe disease

Climate-Immunity Feedback

Changes in transmission intensity (climate-driven) affect immunity:

  • Increased transmission → Immunity builds faster → Fewer clinical cases (paradoxically)
  • Decreased transmission → Immunity wanes → More clinical cases when transmission resumes

This can cause epidemic rebound after control efforts or climate fluctuations.

1.5 Environmental Factors

💧 Surface Hydrology

Breeding sites are the foundation of mosquito populations. Their formation, persistence, and productivity depend on hydrology.

Breeding Habitat Types

Temporary Pools (Rain-Dependent)

  • Formation: Rainfall creates pools in depressions
  • Persistence: Days to weeks (evaporation, infiltration)
  • Productivity: High (few predators, optimal conditions)
  • Climate Sensitivity: Very high
  • Mosquito Species: An. gambiae s.s. (Africa), An. funestus (when drying)

Permanent Water (Rivers, Lakes, Wetlands)

  • Formation: Year-round water availability
  • Persistence: Months to permanent
  • Productivity: Lower (more predators, vegetation)
  • Climate Sensitivity: Moderate (flow and level vary)
  • Mosquito Species: An. funestus, An. arabiensis

Irrigation Systems (Human-Managed)

  • Formation: Agriculture (rice, vegetables)
  • Persistence: Seasonal to year-round
  • Productivity: Very high (nutrient-rich, stable)
  • Climate Sensitivity: Low (managed)
  • Mosquito Species: All Anopheles species

Pond Dynamics Model

VECTRI represents breeding sites as a fractional pond coverage:

\[ \frac{dw}{dt} = K_w \times \left[R_d (w_{max} - w) - w(E + I)\right] \]

Where:

  • \(w\) = pond fraction (0-0.04 typically)
  • \(R_d\) = daily rainfall (mm)
  • \(E\) = evaporation rate (mm/day)
  • \(I\) = infiltration rate (mm/day)
  • \(w_{max}\) = maximum pond coverage (~4%)

Seasonal Pattern

Dry Season:

  • Evaporation > Rainfall → \(dw/dt < 0\)
  • Ponds shrink and disappear
  • Vector populations collapse
  • Transmission ceases

Wet Season Onset:

  • Rainfall > Evaporation → \(dw/dt > 0\)
  • Ponds form and expand
  • Vector populations explode (1-2 months lag)
  • Transmission intensifies

Peak Wet Season:

  • Abundant habitat (high \(w\))
  • But heavy flushing reduces larval survival
  • Transmission moderate despite high rainfall

End of Wet Season:

  • Moderate rainfall, low flushing
  • Optimal conditions for transmission
  • Often the peak transmission period

1.6 Modeling Malaria Transmission

🖥️ Overview of Malaria Models

Mathematical models are essential for understanding malaria transmission dynamics and forecasting.

Why Model Malaria?

"Malaria transmission is too complex to understand intuitively. Models provide a rigorous framework to integrate multiple processes, test hypotheses, and forecast under changing conditions."

Model Categories

Characteristics:

  • Based on observed patterns
  • Correlate climate with malaria cases
  • No explicit biology

Strengths:

  • Simple, fast
  • Good for short-term forecasts
  • Data-driven

Weaknesses:

  • Limited mechanistic understanding
  • Poor extrapolation outside training data
  • Cannot simulate interventions

Examples:

  • Regression models (climate → cases)
  • Machine learning (random forests, neural nets)

Characteristics:

  • People divided into compartments (S-E-I-R)
  • Differential equations for flows
  • Simplified vector dynamics

Strengths:

  • Mechanistic insights
  • Can test interventions
  • Well-established theory

Weaknesses:

  • Often spatially aggregated
  • Simplified vector biology
  • Difficult to parameterize

Examples:

  • Ross-Macdonald model
  • SEIR models with vector compartments

Characteristics:

  • Explicit vector life cycle (larvae, adults)
  • Climate-driven rates and survival
  • Spatially explicit (gridded)
  • Coupled to transmission in humans

Strengths:

  • Most realistic biology
  • Climate-responsive
  • Spatial dynamics captured
  • Can forecast under novel conditions

Weaknesses:

  • Complex (many parameters)
  • Computationally intensive
  • Requires detailed climate data

Examples:

  • VECTRI (our focus!)
  • Liverpool Malaria Model (LMM)
  • Hydrology-based models

VECTRI Model Overview

What is VECTRI?

VECTRI = VECtor-borne disease community model of ICTP, TRIeste

A dynamical, climate-driven model that simulates:

  1. Breeding site hydrology (rainfall, evaporation, infiltration)
  2. Larval dynamics (development, survival, crowding, flushing)
  3. Adult mosquito populations (emergence, survival, biting)
  4. Parasite development (sporogonic cycle, EIP)
  5. Human infection dynamics (susceptible, infected, immune)
  6. Spatial spread (adult dispersal, human movement)

Key Features:

  • Climate inputs: Temperature, rainfall, humidity
  • Resolution: Typically 0.5° (~50 km) grid
  • Time step: Daily
  • Outputs: EIR, prevalence, clinical cases, vector density

Model Applications

  • Seasonal forecasting: Predict transmission 1-6 months ahead
  • Climate change impacts: Project future transmission zones
  • Intervention planning: Optimize timing and targeting of control
  • Epidemic early warning: Detect anomalous conditions

🎬 Summary

Congratulations! You've completed the theoretical foundation for climate-driven malaria modeling.

Key Takeaways

✅ Climate-Transmission Links

  • Temperature, rainfall, and humidity control vector and parasite biology
  • Nonlinear responses create critical thresholds
  • Transmission requires simultaneous favorable conditions

✅ Vector Life Cycle

  • Larvae: Temperature-dependent development, crowding, flushing
  • Adults: Temperature-dependent survival, gonotrophic cycle
  • Critical: Mosquito lifespan must exceed parasite EIP

✅ Parasite Biology

  • 16°C minimum threshold for development
  • EIP decreases exponentially with temperature
  • Long EIP at cooler temperatures limits transmission

✅ Host Factors

  • Population density affects biting rates (nonlinear)
  • Immunity builds with exposure, wanes without
  • Age patterns reflect cumulative exposure

✅ Environmental Drivers

  • Hydrology creates breeding habitat
  • Balance of rainfall, evaporation, infiltration
  • Seasonal patterns drive transmission cycles

✅ Modeling Approaches

  • Dynamical models like VECTRI capture climate-biology links
  • Essential for forecasting and intervention planning
  • Bridge climate science and public health

🔜 Next Steps

Now that you understand the theory, you're ready to explore VECTRI in action!

  • Explore Use Cases

    See how VECTRI is applied to real-world malaria forecasting

    View Use Cases →

  • Get Started with VECTRI

    Introduction to the VECTRI model structure and components

    VECTRI Introduction →

  • Deep Dive: Model Components

    Detailed equations and implementations for all VECTRI components

    Model Components →

📚 References and Further Reading

Foundational Papers

  • Ross, R. (1911). The Prevention of Malaria. London: John Murray. (Original mathematical framework)
  • Macdonald, G. (1957). The Epidemiology and Control of Malaria. Oxford University Press. (Refined transmission model)
  • Martens, W.J.M. et al. (1995). Potential impact of global climate change on malaria risk. Environmental Health Perspectives, 103(5), 458-464. (Survival model)
  • Bayoh, M.N. & Lindsay, S.W. (2003). Effect of temperature on the development of the aquatic stages of Anopheles gambiae sensu stricto. Bulletin of Entomological Research, 93(5), 375-381. (Larval development)

VECTRI-Specific

  • Tompkins, A.M. & Ermert, V. (2013). A regional-scale, high resolution dynamical malaria model that accounts for population density, climate and surface hydrology. Malaria Journal, 12, 65. (Original VECTRI paper)
  • Tompkins, A.M. & Thomson, M.C. (2018). Uncertainty in malaria simulations in the highlands of Kenya: Relative contributions of model parameter setting, driving climate and initial condition errors. PLOS ONE, 13(9). (Uncertainty analysis)

Additional Resources

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