THE VET VORTEX ANALYSIS CASE STUDY

GLOBAL AND REGIONAL (NIGERIA) SPRINGTIME PET ALLERGIES: A DATA-DRIVEN LOOK AT SEASONAL REACTIONS

SCENARIO

As the founder and content strategist of The Vet Vortex, I aim to analyze how seasonal pet allergies, especially during spring, are represented in public search interest globally and in Nigeria. With a strong data foundation from Google Trends, this case study explores the seasonality, species-specific trends, and keyword behavior related to pet allergy concerns. This insight guides educational content, helps raise public awareness, and informs SEO strategy.

BACKGROUND:

The Vet Vortex is a pet health blog designed to demystify veterinary concepts, spotlight zoonotic disease awareness and support pet owner education. In line with our mission to combine veterinary science with public interest, we’re leveraging Google Trends to monitor how pet emergencies capture attention over time.

This study focuses on the global landscape, while a parallel seasonal report will analyze Nigeria-specific search behaviors. By identifying how interest varies across species (dogs, cats, rabbits, birds, reptiles, and small mammals), we provide actionable insights to the public and veterinary stakeholders.

ASK PHASE

Business Objectives

  1. Identify spring time trends in pet allergy-related searches globally and in Nigeria.
  2. Uncover species-specific patterns (dogs, cats, rabbits, birds, rodents).
  3. Support pet owner education with actionable insights and data-backed blog content.
  4. Strengthen SEO with seasonally relevant, high-interest keywords.

Key stakeholders:

1.The Vet Vortex editorial and analytics team. 2. Pet owners worldwide (global audience). 3. Nigerian pet owners and veterinarians (local audience). 4. Veterinary professionals and public health advocates. 5. Animal welfare educators and online pet communities.

PREPARE PHASE

Keyword List Creation

A structured list of 100 pet allergy-related keywords(search terms) were curated into thematic groups, including:

  • Global & Seasonal Allergy Trends
  • Data & Analysis-Specific
  • Specie specific - Dogs, Cats, Rabbits, Birds, Guinea Pigs, Rodents.
  • Environmental Triggers
  • Veterinary & Clinical Terms

These keywords reflect common concerns like sneezing, itchy skin, pollen, hay, bedding allergies, and reactions to spring blooms. They were saved under variables global_allergy_keywords and nigeria_allergy_keywords.

Emergency Types and Reactions Covered

While not traditionally classified as “emergencies,” seasonal allergy searches were grouped based on symptom severity and urgency:

  • Respiratory issues (sneezing, coughing)
  • Dermatological issues (itching, rash, hair loss)
  • Ophthalmic signs (red eyes, discharge)
  • Immune system responses (general allergic reaction, dander sensitivity)

Time frame

This analysis covers a 10-year timeframe spanning from June 1, 2015 — May 31, 2025. This precise 10-year period captures the evolution of seasonal allergy awareness and reveals consistent annual spring spikes, particularly during March–May.

Data Collection and Source

  • The data was collected using the Google Trends API and the gtrendsR() Package, which provides real-Time search.
  • The data was collected from:
  1. Global
  2. Nigeria

Does data ROCC

  • Reliable - Yes, Google trends is a reliable source of data for search trends.
  • Original - Yes, directly queried from Google’s database.
  • Comprehensive - Yes, data covers a wide range of pet allergies and species with a large time frame and two regions (Global and Nigeria).
  • Current - Yes, includes data up to May 2025.
  • Cited - Yes, data citation is possible via gtrendsR query metadata and GitHub logs.

Methodology

  1. Data Preprocessing
  • Top Hits: Filtered by highest interest scores to identify most searched seasonal allergy terms globally and in Nigeria.
  • Bottom Hits: Included keywords with low or zero searches to highlight underrecognized pet allergy concerns.
  1. Species-level grouping
  • Separate visualizations and tables for dogs, cats, rabbits, birds, guinea pigs, and rodents to reveal species-specific interest trends.
  1. Spring Focus
  • Extracted search interest for March, April, and May annually to analyze springtime allergy spikes.
  • Identified the peak month of search activity for each species and keyword group.
  1. Trend Visualization Time-series plots and heatmaps created to show:
  • Interest over time
  • Year-over-year growth
  • Species vs. keyword clusters

Limitations

  1. Relative Interest, Not Absolute Counts – Google Trends provides normalized data, not total search volumes.
  2. Geographic Bias – Internet usage patterns may skew representation, especially in under-connected regions.
  3. Language Bias – Non-English searches may be underrepresented; keywords were English-only.
  4. Search Intent Ambiguity – Search phrases may not always imply emergency context.
  5. Data Gaps – Low-interest terms in some regions may show incomplete data.
  6. Keyword Selection Bias - Keyword lists were curated manually, which may have excluded relevant but less obvious emergency terms, especially in regional contexts.
  7. Temporal Bias - The analysis is limited to a specific time frame, which may not capture all seasonal trends or emerging issues. External events (viral videos, pet safety campaigns, etc.) may artificially inflate search interest unrelated to true incident rates. 8 Incomplete or Sparse Keyword Batches - Batches 13 and 14, containing keywords like “reptile constipation” and “guinea pig not eating”, returned valid data but showed consistently zero search interest over the entire 10-year span. This indicates extremely low or nonexistent public interest in these specific pet emergencies within Nigeria.
  8. Absence of demographic information in the dataset This would have provided additional context on the types of pet owners searching for these terms. Additionally, the analysis does not account for external factors, such as public health campaigns or events (e.g., COVID-19), that may have influenced search behavior during certain periods. Notably, COVID-19 was frequently referenced during the analysis as a potential driver of increased search volumes, likely due to heightened health awareness, lockdown-induced pet ownership spikes, and limited access to veterinary services during certain periods.

Batch 15 failed entirely after six retry attempts, returning a NULL interest_over_time object each time. This could be due to temporary rate limits or genuinely nonexistent search volume for the terms in that batch. (guinea pig bloat”, “hamster dehydration”, “guinea pig difficulty breathing”, “gerbil not moving”, “rodent overheating”)

These results may reflect regional pet ownership trends, where exotic pets are less common and thus less represented in public search behavior. While their exclusion slightly limits coverage of rarer pet emergencies, the remaining dataset still robustly represents the dominant concerns of Nigerian pet owners.

note

In this analysis, keywords were queried in separate batches of five due to limitations of the Google Trends API. Each batch was scaled independently, meaning the “hits” values (ranging from 0 to 100) represent relative public interest within each individual batch - not across all batches.

Specifically, a hit value of 100 indicates the peak search interest among the five keywords in that batch during the selected time frame, while lower values represent proportionally less interest.

I did not standardize keywords across batches using an anchor term, so direct comparison of hit values between different batches is not valid.

Instead, keywords are analyzed and ranked based on their total “hits” over time, which gives a general sense of overall public attention — useful for identifying high-interest topics, but not for making precise comparisons between search volumes.

Therefore, results should be interpreted as reflecting relative patterns of interest, not exact or absolute search volume.

PROCESSING PHASE

Tools to used are : a) R- To manipulate, clean and analyze data. b) Tableau- For some data visualization

Data manipulation and cleaning.

I. Data Manipulation

  1. load relevant/necessary libraries
library(gtrendsR) # to access Google Trends
library(dplyr)  # for data manipulation/wrangling
library(ggplot2)  # for plotting/visualization
library(zoo)  # for moving/rolling averages
library(R.utils) # for file download
library(readr) # for reading CSV files
library(tidyverse) # for data manipulation
library(lubridate) # for data manipulation
library(janitor) # for data cleaning
library(stringr) # for string manipulation
  1. Define Keywords and Time Range
# 🌍 Global & Seasonal Allergy Trends
global_keywords <- c(
  "global pet allergy trends spring",
  "seasonal allergies in pets worldwide",
  "spring pet allergy incidence data",
  "international pet allergy statistics",
  "end of May pet allergy trends",
  "allergy season pets global",
  "springtime allergy patterns in pets",
  "seasonal pet reactions spring",
  "pollen season pet symptoms",
  "May allergy trends in pets"
)

# 📊 Data & Analysis-Specific
analysis_keywords <- c(
  "pet allergy data analysis",
  "allergy season trends in pets",
  "spring allergy stats for dogs",
  "pet allergy infographics",
  "seasonal pet allergy chart",
  "allergy pattern visualization pets",
  "global spring allergy data pets",
  "pet allergy case studies spring",
  "pet allergy research global",
  "pet allergy patterns over time"
)

# 🐶 Dog-Specific Keywords
dog_keywords <- c(
  "dog spring allergies symptoms",
  "itchy skin in dogs spring",
  "dog sneezing in spring",
  "pollen allergies in dogs",
  "dog paw licking allergies spring",
  "dog red eyes seasonal allergy",
  "dog antihistamines spring",
  "dog skin rash from allergies",
  "spring allergy medicine for dogs",
  "dog seasonal allergy relief"
)

# 🐱 Cat-Specific Keywords
cat_keywords <- c(
  "cat spring allergies",
  "cat sneezing springtime",
  "itchy cat skin spring",
  "cat watery eyes allergies",
  "cat seasonal allergy symptoms",
  "cat antihistamines",
  "cat scratching allergies spring",
  "cat allergy rash in May",
  "pollen allergies in cats",
  "cat allergy treatment trends"
)

# 🐰 Rabbit-Specific Keywords
rabbit_keywords <- c(
  "rabbit seasonal allergies",
  "rabbit sneezing in spring",
  "hay allergies in rabbits",
  "bedding allergies in rabbits",
  "rabbit itchy nose spring",
  "springtime allergy in pet rabbits",
  "rabbit red eyes allergy",
  "rabbit allergy symptoms spring",
  "rabbit allergy trends",
  "rabbit respiratory issues spring"
)

# 🐦 Bird-Specific Keywords
bird_keywords <- c(
  "bird feather allergy",
  "dust allergy in birds",
  "cockatiel spring allergies",
  "parrot sneezing in spring",
  "bird seasonal allergy patterns",
  "bird pollen sensitivity",
  "avian allergy symptoms spring",
  "bird allergy incidence May",
  "springtime feather dander issues",
  "pet bird allergy reactions"
)

# 🐹 Guinea Pig-Specific Keywords
gpig_keywords <- c(
  "guinea pig sneezing in spring",
  "hay allergy in guinea pigs",
  "guinea pig spring allergy symptoms",
  "guinea pig seasonal allergy data",
  "guinea pig bedding sensitivity",
  "itchy guinea pig spring",
  "guinea pig pollen exposure",
  "guinea pig allergy trends",
  "guinea pig red eyes allergies",
  "guinea pig spring skin irritation"
)

# 🐭 Rodent-Specific (Hamsters, Gerbils, etc.)
rodent_keywords <- c(
  "hamster spring allergy symptoms",
  "gerbil sneezing spring",
  "pet rodent dust allergy",
  "allergy in small rodents",
  "rodent bedding allergy spring",
  "hamster allergy incidence May",
  "exotic pet allergies spring",
  "springtime allergies in gerbils",
  "rodent skin allergy spring",
  "allergic reactions in small pets"
)

# 🌸 Environmental & Seasonal Allergy Triggers
trigger_keywords <- c(
  "pollen season pet allergy",
  "grass allergy in pets",
  "mold allergy in pets",
  "dust allergy in animals",
  "spring bloom pet reaction",
  "May weather and pet allergies",
  "airborne allergens pets",
  "high pollen count pet symptoms",
  "seasonal triggers pet sneezing",
  "pet allergy increase in spring"
)

# 🏥 Veterinary & Clinical Terms
vet_keywords <- c(
  "veterinary allergy diagnosis",
  "veterinary allergy treatment",
  "veterinary allergy statistics",
  "veterinary antihistamines",
  "pet allergy relief medicine",
  "pet allergy management spring",
  "vet dermatology spring allergies",
  "immunotherapy for pet allergies",
  "spring veterinary visits for allergies",
  "spring allergy consultation for pets"
)

# Combine all keyword sets into one vector
allergy_keywords <- c(
  global_keywords, analysis_keywords, dog_keywords, cat_keywords, rabbit_keywords,
  bird_keywords, gpig_keywords, rodent_keywords, trigger_keywords, vet_keywords
)

# Save to CSV for documentation and reproducibility
write.csv(data.frame(keyword = allergy_keywords), "pet_allergy_keywords.csv", row.names = FALSE)

# Define 10-year time range
time_range <- "2015-06-01 2025-05-31"
  1. Fetch Google Trends Data for defined keywords and time frame
  1. For Global data
# split keywords into batches of 5; Google only allows 5 terms per request. # 100 keywords into 20 batches.


# Function to split keywords into 5 batches
split_keywords <- function(keywords, batch_size = 5) {
  split(keywords, ceiling(seq_along(keywords) / batch_size))
}

# Batch keywords
keyword_batches <- split_keywords(allergy_keywords, 5) 

# Store results
trend_data_list <- vector("list", length(keyword_batches))

# Fetch data with smart retries, backoff, jitter, and normalization
for (i in seq_along(keyword_batches)) {
  cat(Sys.time(), "- Starting batch", i, "of", length(keyword_batches), "...\n")
  
  success     <- FALSE
  wait_time   <- 300    # 5 minutes
  max_wait    <- 3600   # up to 60 minutes
  max_retries <- 6
  attempts    <- 0
  
  while (!success && attempts < max_retries) {
    attempts <- attempts + 1
    
    tryCatch({
      # Fetch with timeout
      result <- withTimeout(
        gtrends(
          keyword = keyword_batches[[i]],
          time    = time_range,
          geo     = "",
          gprop   = "web"
        ),
        timeout   = 120,
        onTimeout = "error"
      )
      
      # Normalize hits column: "<1" → 0.5, else numeric
      df <- result$interest_over_time %>%
        mutate(
          hits = ifelse(hits == "<1", "0.5", hits),
          hits = as.numeric(hits)
        )
      
      trend_data_list[[i]] <- df
      success <- TRUE
      
      # Save only current batch result separately
      write.csv(df,
                paste0("temp_partial_batch_global_", i, ".csv"),
                row.names = FALSE)
      
      cat(Sys.time(),
          "- Batch", i, "completed (attempt", attempts, ").\n")
      
      # Fixed 15 min pause + jitter ±60 s
      pause <- 900 + sample(-60:60, 1)
      cat("Sleeping for", round(pause/60,1), "minutes...\n")
      Sys.sleep(pause)
      
    }, error = function(e) {
      cat(Sys.time(),
          "- Error on batch", i,
          "(attempt", attempts, "):", conditionMessage(e), "\n")
      
      # Exponential backoff + jitter
      jitter    <- sample(-30:30, 1)
      sleep_time <- min(wait_time * 2^(attempts - 1), max_wait) + jitter
      cat("Sleeping for", round(sleep_time/60,1),
          "minutes before retrying...\n")
      Sys.sleep(sleep_time)
    })
  }
  
  if (!success) {
    cat("❌ Batch", i,
        "failed after", attempts, "attempts — skipping.\n")
    trend_data_list[[i]] <- NULL
  }
}

# Combine & save final results
all_trends <- bind_rows(trend_data_list)
write.csv(all_trends,
          "global_pet_allergy_trends.csv",
          row.names = FALSE)

cat(Sys.time(), "- ✅ All done! Data saved to global_pet_allergy_trends_.csv\n")

Global data is saved as global_pet_allergy_trends

  1. For Nigeria data
keyword_batches_ng <- split_keywords(allergy_keywords, 5)
trend_data_list_ng <- vector("list", length(keyword_batches_ng))

# Loop through batches
for (i in seq_along(keyword_batches_ng)) {
  cat(Sys.time(), "- [NG] Starting batch", i, "of", length(keyword_batches_ng), "...\n")
  
  success     <- FALSE
  wait_time   <- 300
  max_wait    <- 3600
  max_retries <- 6
  attempts    <- 0
  
  while (!success && attempts < max_retries) {
    attempts <- attempts + 1
    
    tryCatch({
      result_ng <- withTimeout(
        gtrends(
          keyword = keyword_batches_ng[[i]],
          time    = time_range,
          geo     = "NG",  # Nigeria-specific
          gprop   = "web"
        ),
        timeout   = 120,
        onTimeout = "error"
      )
      
      df_ng <- result_ng$interest_over_time %>%
        mutate(
          hits = ifelse(hits == "<1", "0.5", hits),
          hits = as.numeric(hits)
        )
      
      trend_data_list_ng[[i]] <- df_ng
      success <- TRUE
      
      # Save only current batch result separately
      write.csv(df_ng,
                paste0("temp_partial_batch_nigeria_", i, ".csv"),
                row.names = FALSE)
      
      cat(Sys.time(),
          "- [NG] Batch", i, "completed (attempt", attempts, ").\n")
      
      pause <- 900 + sample(-60:60, 1)
      cat("[NG] Sleeping for", round(pause/60,1), "minutes...\n")
      Sys.sleep(pause)
      
    }, error = function(e) {
      cat(Sys.time(),
          "- [NG] Error on batch", i,
          "(attempt", attempts, "):", conditionMessage(e), "\n")
      
      jitter <- sample(-30:30, 1)
      sleep_time <- min(wait_time * 2^(attempts - 1), max_wait) + jitter
      cat("[NG] Sleeping for", round(sleep_time/60,1), "minutes before retrying...\n")
      Sys.sleep(sleep_time)
    })
  }
  
  if (!success) {
    cat("❌ [NG] Batch", i, "failed after", attempts, "attempts — skipping.\n")
    trend_data_list_ng[[i]] <- NULL
  }
}

# Combine & save final Nigeria results
all_trends_ng <- bind_rows(trend_data_list_ng)
write.csv(all_trends_ng,
          "nigeria_pet_allergy_trends.csv",
          row.names = FALSE)

cat(Sys.time(), "- ✅ [NG] All done! Data saved to nigeria_pet_allergy_trends.csv\n")

Nigeria data is saved as nigeria_pet_allergy_trends

  1. Data Transformation
# Load the datasets
global_data <- read_csv("global_pet_allergy_trends.csv")
nigeria_data <- read_csv("nigeria_pet_allergy_trends.csv")

# Add a new column to identify source if not already present
global_data <- global_data %>% mutate(region = "Global")
nigeria_data <- nigeria_data %>% mutate(region = "Nigeria")

# Combine datasets by rows
combined_data <- bind_rows(global_data, nigeria_data)


# Categorization function for allergy keywords

categorize_allergy <- function(keyword) {
  
  if (grepl("global pet allergy trends spring|seasonal allergies in pets worldwide|spring pet allergy incidence data|international pet allergy statistics|end of May pet allergy trends|allergy season pets global|springtime allergy patterns in pets|seasonal pet reactions spring|pollen season pet symptoms|May allergy trends in pets", keyword, ignore.case = TRUE)) {
    return("global & seasonal allergy trends")
  }
  if (grepl("pet allergy data analysis|allergy season trends in pets|spring allergy stats for dogs|pet allergy infographics|seasonal pet allergy chart|allergy pattern visualization pets|global spring allergy data pets|pet allergy case studies spring|pet allergy research global|pet allergy patterns over time", keyword, ignore.case = TRUE)) {
    return("data & analysis")
  }
  if (grepl("dog spring allergies symptoms|itchy skin in dogs spring|dog sneezing in spring|pollen allergies in dogs|dog paw licking allergies spring|dog red eyes seasonal allergy|dog antihistamines spring|dog skin rash from allergies|spring allergy medicine for dogs|dog seasonal allergy relief", keyword, ignore.case = TRUE)) {
    return("dog-specific")
  }
  if (grepl("cat spring allergies|cat sneezing springtime|itchy cat skin spring|cat watery eyes allergies|cat seasonal allergy symptoms|cat antihistamines|cat scratching allergies spring|cat allergy rash in May|pollen allergies in cats|cat allergy treatment trends", keyword, ignore.case = TRUE)) {
    return("cat-specific")
  }
  if (grepl("rabbit seasonal allergies|rabbit sneezing in spring|hay allergies in rabbits|bedding allergies in rabbits|rabbit itchy nose spring|springtime allergy in pet rabbits|rabbit red eyes allergy|rabbit allergy symptoms spring|rabbit allergy trends|rabbit respiratory issues spring", keyword, ignore.case = TRUE)) {
    return("rabbit-specific")
  }
  if (grepl("bird feather allergy|dust allergy in birds|cockatiel spring allergies|parrot sneezing in spring|bird seasonal allergy patterns|bird pollen sensitivity|avian allergy symptoms spring|bird allergy incidence May|springtime feather dander issues|pet bird allergy reactions", keyword, ignore.case = TRUE)) {
    return("bird-specific")
  }
  if (grepl("guinea pig sneezing in spring|hay allergy in guinea pigs|guinea pig spring allergy symptoms|guinea pig seasonal allergy data|guinea pig bedding sensitivity|itchy guinea pig spring|guinea pig pollen exposure|guinea pig allergy trends|guinea pig red eyes allergies|guinea pig spring skin irritation", keyword, ignore.case = TRUE)) {
    return("guinea pig-specific")
  }
  if (grepl("hamster spring allergy symptoms|gerbil sneezing spring|pet rodent dust allergy|allergy in small rodents|rodent bedding allergy spring|hamster allergy incidence May|exotic pet allergies spring|springtime allergies in gerbils|rodent skin allergy spring|allergic reactions in small pets", keyword, ignore.case = TRUE)) {
    return("rodent-specific")
  }
  if (grepl("pollen season pet allergy|grass allergy in pets|mold allergy in pets|dust allergy in animals|spring bloom pet reaction|May weather and pet allergies|airborne allergens pets|high pollen count pet symptoms|seasonal triggers pet sneezing|pet allergy increase in spring", keyword, ignore.case = TRUE)) {
    return("environmental & seasonal triggers")
  }
  if (grepl("veterinary allergy diagnosis|veterinary allergy treatment|veterinary allergy statistics|veterinary antihistamines|pet allergy relief medicine|pet allergy management spring|vet dermatology spring allergies|immunotherapy for pet allergies|spring veterinary visits for allergies|spring allergy consultation for pets", keyword, ignore.case = TRUE)) {
    return("veterinary & clinical terms")
  }
  
  # If no match, return "uncategorized"
  return("uncategorized")
}

# Apply categorization to combined dataset
combined_pet_allergy_trends <- combined_data %>%
  mutate(allergy_category = sapply(keyword, categorize_allergy))

# Parse date and extract month-year and year
combined_pet_allergy_trends <- combined_pet_allergy_trends %>%
  mutate(
    date = as.Date(date),
    month_year = floor_date(date, unit = "month"),
    year = year(date)
  )

# Save final combined and categorized data
write_csv(combined_pet_allergy_trends, "combined_pet_allergy_trends.csv")

cat("✅ Combined global + Nigeria data with categories saved as combined_pet_allergy_trends.csv\n")
  1. check the data
# check data 
str(combined_pet_allergy_trends) # structure of the data
glimpse(combined_pet_allergy_trends) # glimpse of the data
head(combined_pet_allergy_trends) # first few rows of the data
  1. Data Cleaning
# Step 1: Remove unnecessary columns
cleaned_combined_pet_allergy_trends <- combined_pet_allergy_trends %>%
  select(-time, -category, -gprop)

# Step 2: Remove names from allergy_category (was named vector)
cleaned_combined_pet_allergy_trends$allergy_category <- as.character(cleaned_combined_pet_allergy_trends$allergy_category)
names(cleaned_combined_pet_allergy_trends$allergy_category) <- NULL

# Step 3: Ensure hits is numeric (already numeric, but double-check <1 handling)
# If <1 existed as "<1", it would be character; but from structure, it's numeric already
summary(cleaned_combined_pet_allergy_trends$hits)

# Step 4: Clean column names
cleaned_combined_pet_allergy_trends <- cleaned_combined_pet_allergy_trends %>%
  clean_names()

# Step 5: Check and remove duplicate rows
duplicates_found <- cleaned_combined_pet_allergy_trends %>% duplicated()
cat("Total duplicates found:", sum(duplicates_found), "\n")

cleaned_combined_pet_allergy_trends <- cleaned_combined_pet_allergy_trends %>%
  distinct()

# Step 6: Check for and remove rows with missing values
missing_counts <- colSums(is.na(cleaned_combined_pet_allergy_trends))
print(missing_counts)

cleaned_combined_pet_allergy_trends <- cleaned_combined_pet_allergy_trends %>%
  drop_na()

# Step 7: Reorder columns for clarity
cleaned_trends <- cleaned_combined_pet_allergy_trends %>%
  select(date, month_year, year, geo, keyword, allergy_category, hits)

# Step 8: Final inspection
glimpse(cleaned_trends)
head(cleaned_trends)
summary(cleaned_trends)
  1. Save cleaned data
# save cleaned data -GLOBAL
write.csv(cleaned_trends, "cleaned_trends.csv", row.names = FALSE)

ANALYZE PHASE

  1. Top Allergy by Keywords (Global & Nigeria)

INSIGHTS and OBSERVATIONS

Top Global Keyword: cat antihistamines - Dominates globally, yet barely registers in Nigeria.

Top Nigeria Keyword: immunotherapy for pet allergies - Almost equal interest, showing this is a shared global concern and a strong outlier in Nigeria’s data.

Sharp Global Focus, Nigeria Silence: Over 40 keywords that are trending globally show zero search hits in Nigeria. This points to either:

  • A lack of awareness or public concern about specific allergy terms.

  • Different terminology or phrasing used in Nigerian searches.

  • Lower internet penetration or pet care-related search activity

Caveat: Due to batch-specific normalization, these figures cannot be compared across batches numerically, but their absence in Nigeria’s dataset is still informative.

Nigeria-Specific Emerging Interests: There are keywords with high Nigeria-only hits and 0 global hits such as:

  • itchy cat skin spring

  • rabbit respiratory issues spring

These niche searches suggest that Nigerians are searching about symptoms, not technical terms. More symptom-based public awareness campaigns may resonate better than disease-specific ones.

Symptom-Based vs. Treatment-Based Search Behavior

  • Global Trends = Treatment-Oriented - suggest a solution-seeking mindset—people may already suspect or know the cause and are looking for professional treatment options.

  • Nigeria Trends = Symptom-Oriented - indicating that people may be trying to figure out what’s wrong before they even understand it’s an allergy.

Implication: Public education in Nigeria should start with symptom identifiers THEN educate on cause/treatment.

Species Spotlight

  • Cats Dominate Globally - Cat-specific allergies are most prominent

  • Nigerian Data = More Diverse - High interest in rabbits, guinea pigs, and birds

This supports the idea that maybe:

  • Nigeria may be seeing a rise in exotic pet ownership or concern OR

  • The ambiguity of search terms may also reflect concerns among smallholder livestock owners - particularly in rabbit and poultry farming settings due to it’s strong cultural and economic reliance on food animals.

Zeroes and NAs: What They Tell Us

  • Keywords with 0 global + Nigeria hits (like “spring bloom pet reaction”, “May weather and pet allergies”) = Likely non-relevant or poorly phrased.

  • NA in Global but Hits in Nigeria: These show

  • Nigeria-specific, possibly informal or regional phrasing

  • Suggests that Nigeria’s search behavior includes under-the-radar local keywords that don’t trend globally—valuable.

Summary Table of Notable Patterns

Observation Global Trend Nigeria Trend Actionable Insight
High global concern for cat allergies ✅ ⚠️ Lower interest Local awareness campaigns needed
Symptom-based keywords Less dominant Very dominant Nigeria needs more symptom-explaining content
Rabbit/guinea pig/bird allergy keywords Niche High interest Create species-specific spring guides for exotic pets
Treatment & diagnosis search Strong Weak Vet-focused education (diagnosis & care) is underrepresented in Nigeria
Allergy charts/data/statistics Present Very minimal Educational infographics could fill this void
  1. Top Rising Keywords between 2015 and 2025

INSIGHTS and OBSERVATIONS (2015–2025 Trend Growth)

Global Keyword Trends: Clear Growth in Public Interest

Of the 100 keywords analyzed, only a few showed notable global rise and they are highly specific:

Keyword Rise (Global) 2025 Global Rank (Hits) Notes
cat antihistamines +231 290 Top riser and most searched overall
pollen allergies in dogs +184 247 Strong global concern for canine allergies
cat spring allergies +120 120 Increasing season-specific feline focus
immunotherapy for pet allergies +108 108 Suggests growing interest in medical treatments
global pet allergy trends spring +100 100 Indicates macro-level awareness growing
seasonal pet reactions spring +100 100 Season-specific search surge

Caveat: Due to the batch-based normalization (0–100) in Google Trends, these numbers represent search interest relative to other terms within each batch, not across all keywords. Therefore, while we can confidently highlight that terms like “cat antihistamines” were the most searched within their group and showed strong upward momentum, they are not directly comparable in volume to other keywords in different batches.

Implication: These top risers suggest a global shift toward identifying, managing and preparing for seasonal pet allergies - especially cats and dogs. The sharp rise in interest for treatment-specific terms like antihistamines and immunotherapy reflects a proactive public stance.

Nigerian Trends: Flat or Declining Interest

  • Zero rising trends across nearly all keywords — even those that grew globally.

  • Only one keyword showed growth - cat seasonal allergy symptoms → +40 (from 0 in 2015 to 40 in 2025)

  • A few dropped in interest, such as: itchy cat skin spring, exotic pet allergy spring, etc. → -100;

Possible Causes:

  • Low or no search volume in 2015 and 2025 = low online engagement.

  • Keywords might be too technical or westernized for typical Nigerian search queries.

  • Nigerians may be searching in more symptom-specific, informal, or mobile-friendly phrasing not reflected here.

Takeaway: While global interest surged, Nigeria shows stagnant or declining search behavior, pointing to:

  • A gap in awareness or online health-seeking behavior around pet allergies.

  • An opportunity for local education campaigns

  • A mismatch between global keyword formats and Nigeria’s informal search styles

Keywords That Declined Globally These dropped from high in 2015 to zero by 2025:

Keyword Drop
guinea pig allergy trends -100
hay allergy in guinea pigs -100
rabbit allergy trends -100
seasonal pet allergy chart -95

Interpretation:

  • These topics may no longer be trending globally or have shifted into new phrasing (e.g., “pet seasonal allergy data” or “infographics”).

  • It could also reflect declining interest in less common pets (rabbits, guinea pigs), at least in global data. OR

  • A data artifact - since these terms performed poorly within their batches, and due to Google Trends’ inability to provide absolute values, the low numbers might represent limited visibility rather than true irrelevance.

Rise of Medical/Scientific Terminology in Global Searches

Keywords like:

  • immunotherapy for pet allergies

  • veterinary antihistamines

  • cat antihistamines

  • pet allergy relief medicine

reflect a global trend toward more advanced, informed pet parenting. People aren’t just Googling “itchy dog” anymore. They’re exploring diagnostic and therapeutic language. This aligns with:

  • Greater veterinary literacy online

  • Increased access to pet healthcare content

  • More people treating pets as family members

  1. Top Allergy by Allergy Category

INSIGHTS and OBSERVATIONS

Global vs. Nigeria

1. Cat-Specific Allergies Dominate Globally, Lag in Nigeria - Cat-related allergy queries are by far the most searched globally, showing strong global concern. In Nigeria, they’re noticeably lower, possibly due to:

  • Lower cat ownership rates

  • Underreporting or lack of awareness

  • Cultural or religious perceptions around cats

Interpretation Caution: Since each batch is scaled independently, global dominance refers to intra-batch patterns, not universal search supremacy.

2. Dog-Specific Allergies Are the Second Most Common Globally - Dogs are the second most common species in allergy searches worldwide. Similar to cats, Nigeria shows significantly lower search interest, though dogs are more culturally common. This suggests:

  • Allergic symptoms may be misattributed or normalized

  • Limited public awareness about canine environmental allergies

  • Under-diagnosis due to limited access to veterinary allergy diagnostics

3. Veterinary & Clinical Terms Get More Attention in Nigeria - Though globally higher, Nigeria shows a relatively high proportion of interest in clinical terms compared to other categories. Indicates:

  • Search behavior concentrated among more informed pet owners or professionals

  • Preference for clinical or diagnostic terminology over vague symptom searches

  • Growing trust in veterinary consultation or treatment pathways

4. Data & Analysis Terms Rank Surprisingly High in Nigeria - High Nigerian interest in analytical or trend-related terms suggests * Growing digital literacy among pet owners and/or professionals.

  • Professionals, students or bloggers engaging with pet health research

  • Growing local interest in evidence-based or data-driven pet care

5. Environmental & Seasonal Triggers Are Under-Explored - Global audiences increasingly link environmental factors (like pollen, dust, and humidity) to pet allergies, but Nigeria lags. This could signal:

  • Lack of awareness about pollen/dust as allergy causes

  • Search terms may be more species-specific

  • Less media exposure to seasonal allergen awareness campaigns

6. Rabbit-Specific Allergies Are Nigeria’s Top Small Mammal Concern - This is one of the few categories where Nigerian search volume nearly matches global trends. * Rabbit ownership (or exposure) may be more common than assumed.

  • Growing awareness of rabbit-specific respiratory or bedding-triggered allergies

  • Potential overlap with rabbit use in farming, research or as food animals

7. Guinea Pig-Specific Allergies Show Moderate Interest in Nigeria - Guinea pigs generate moderate interest. Could indicate * Rising niche of exotic pet ownership in Nigeria.

  • Interest from specialized owners (e.g., breeders, pet shop customers)

  • Underlying curiosity rather than widespread concern

  • Potential overlap with it’s use in farming, research or as food animals

8. Global & Seasonal Allergy Trends Show Similar Interest - This suggests Nigeria’s shared engagement with global news or seasonal reports suggesting:

  • Broader awareness campaigns may be resonating

  • Nigerians are actively seeking season-specific advice, especially during pollen-heavy months

  • Greater pet owner responsiveness during periods of increased awareness (e.g., Allergy Awareness Month)

9. Bird-Specific Allergy Terms Are Strong in Nigeria - High Nigerian interest suggests either:

  • High exposure to birds (e.g. poultry, parrots) in rural and urban settings

  • Rising concern for bird-associated allergens (e.g. dander, mites)

  • Public health concern around zoonotic issues

  • Potential overlap with it’s use in farming, research or as food animals

10. Rodent Allergies Have a Surprising Twist - This is the only category where Nigeria outpaces the globe—possibly due to:

  • Higher rodent exposure in urban/rural environments

  • Local concern over zoonotic issues (e.g. Lassa fever, rat allergies)

  • Misattribution of household health concerns to pet rodents or pest species

  • Potential overlap with it’s use in farming, research or as food animals This highlights intersection of pet care, pest control and public health in Nigerian search behavior.

Summary Table of Patterns

Category Global Rank Nigeria Rank Nigeria-Relevant Insight
Cat-Specific 1st 4th Low awareness or ownership
Dog-Specific 2nd 6th Less recognized allergies
Veterinary & Clinical 3rd 3rd High interest in terms
Data & Analysis 4th 5th Research-driven searches
Environmental & Seasonal 5th 7th Underrecognized triggers
Rabbit-Specific 6th 2nd Surprisingly popular
Guinea Pig-Specific 7th 8th Growing pet trend
Global & Seasonal Trends 8th 1st Matched global curiosity
Bird-Specific 9th 9th Rural & pet exposure
Rodent-Specific 10th 🔺Top 3? Nigeria-only concern
  1. Monthly Trend for each year (2015–2025)

INSIGHTS and OBSERVATIONS

Seasonal Trend

1. Spring Dominates Globally But Barely Registers in Nigeria

  • Spring consistently shows the highest global hit counts, peaking in 2024 and holding the top spot in 8 out of 11 years.

  • Meanwhile, Nigeria records zero or near-zero hits for Spring in 8 out of 11 years, with its highest in 2019.

Implication: Despite spring being peak allergy season globally, Nigeria’s public awareness or online interest remains surprisingly low. This may be due to:

  • Underdiagnosis,

  • Low allergy testing rates in pets, or

  • Poor digital literacy on pet health topics.

2. Summer Awareness Grows Globally—Nigeria Starts to Catch Up in 2024

  • Global summer hits remain high and consistent from 2018 onward, with a clear rising trend from 2021 to 2024.

  • Nigeria starts showing a notable summer uptick in 2024 after nearly a decade of negligible interest.

Implication: Summer allergies may be becoming more recognized in Nigeria, possibly linked to increasing pet ownership, climate change or public education campaigns.

3. Autumn: Globally Low, Nigeria Nearly Absent Until 2021

  • Global autumn hits fluctuate but remain lower compared to Spring and Summer.

  • Nigeria only starts registering Autumn interest in 2021 and stays low thereafter.

Implication: Autumn allergens (like mold or weed pollens) are not a prominent concern in Nigeria, possibly due to differences in plant species or climatic patterns.

4. Winter Surprises: Nigeria Sometimes Leads Global Hits

Note:

Using Northern Hemisphere season labels (spring, summer, autumn, winter), Winter in West Africa-Nigeria is ≠ snow and cold, but instead represents Harmattan - a dry, dusty season characterized by:

  • Cold, dry continental air

  • Dust-laden winds from the Sahara

  • Low humidity and sudden temperature fluctuations

These are environmental stressors that:

  • Trigger respiratory issues

  • Exacerbate allergies (especially dust, mold, and dryness)

  • Lead to increased pet owner concern for skin, respiratory and eye symptoms in pets

Observation & insights:

Based on the above,

  • “Winter” is the only season where Nigeria occasionally surpasses global allergy-related search interest — notably in 2016 and 2017.

  • This inverse trend suggests Nigeria allergy patterns are driven by different environmental triggers - particularly dust, low humidity and indoor air quality, rather than pollen or cold weather.

Implication: Harmattan may function as a distinct allergy season in Nigeria, one that is underrepresented or misclassified in global trend analyses based on temperate-season frameworks.

Yearly Trend

1. 2024 Was a Record-Breaking Year for Global Awareness

  • Every season in 2024 shows strong global numbers.

  • This spike may reflect post-pandemic digital health engagement, improved pet allergy testing or climate change impacts.

2. Nigeria’s Awareness Remains Sporadic, Except in 2019 and 2024

  • 2019 and 2024 stand out as the only years with across-season awareness spikes in Nigeria.

  • The other years show inconsistent or zero hits, especially for Spring.

Implication: Allergy-related awareness in Nigeria may be tied to isolated media coverage, influencer campaigns or public health events.

3. 2025 Outlook: Promising or Concerning?

  • Spring 2025 global interest remains strong but Nigeria’s numbers dropped again, following a downward pattern from previous years.

Implication: Despite global consistency, Nigeria’s spring allergy awareness may be regressing or signaling stagnation in 2025.

  1. Spring Months trend (March, April, May) Yearly

INSIGHTS and OBSERVATIONS

Spring Allergy Search Trends (March–May, 2016–2025)

1. Global Spring Allergy Awareness Is Rising Sharply

  • Global hits steadily increased from March 2016 to March–May 2024, showing growing public concern or awareness about springtime pet allergies globally.

  • March 2024 hit a record which is more than 2.5× the count in March 2020.

  • Year-over-year growth in global springtime hits is consistent, peaking in recent years (especially 2022–2024), suggesting seasonal allergies are now a mainstream pet health topic globally.

Implication: Springtime pet allergy awareness is growing steadily, particularly in March, likely due to pollen surges, seasonal shedding and increased outdoor pet activity.

2. Nigeria’s Search Interest Remains Sporadic and Minimal

  • Across 10 years, Nigeria recorded zero hits in 67% of spring months (20 out of 30 data points).

  • Only 5 months (all between 2017 and 2025) show any search hits from Nigeria, with April 2019 being the only major spike.

  • 2024 saw a modest bump with April, while 2025 returned to low levels in April, March and May.

Implication: Nigerian interest is episodic, not seasonal and may be driven more by isolated events, media coverage or outbreaks than by an annual spring trend.

3. April Is the Peak Allergy Month Globally, Nigeria Follows When It Does Spike

  • Globally, April consistently ranks among the top-hit months, with a high in 2024.

  • Nigeria’s three most active months were April 2019, April 2022 and April 2024 suggesting April awareness exists, but not every year.

  • March and May remain quiet in Nigeria despite high global activity.

Implication: Nigeria may have a “spring allergy awareness” month, which most aligned with April, not March like the global trend.

4. The Global-Nigeria Difference Is Widening

  • From 2022–2025, the gap between global and Nigeria hits widened dramatically, especially in May

  • Nigeria’s data shows inconsistent engagement, even when global interest surges.

Implication: Despite rising global awareness, Nigeria is not keeping pace in search engagement — a growing “awareness gap” that may indicate limited public education, digital access issues or underestimation of pet allergy issues.

5. Post-Pandemic Awareness Boost (2021–2024)

Global allergy search interest accelerated after 2020, possibly due to:

  • Increased pet adoption during lockdowns

  • More indoor pets developing allergies

  • Greater concern for zoonotic disease overlap (e.g., allergies vs respiratory symptoms)

Implication: The pandemic created a more attentive pet parenting culture. As respiratory symptoms became global headline material, people became more alert to sneezes, coughs and sniffles — even in pets.

  1. Peak Spring Month Yearly (March, April, May)

INSIGHTS and OBSERVATIONS

Global Peak Spring Month Trends (2016–2025)

  • March was the dominant peak month in 4 out of 10 years (2016, 2017, 2019, 2021, 2024).

  • May took the lead in 4 other years (2018, 2020, 2022, 2025).

  • April was only the top month once (2023).

  • The highest global spring hit occurred in 2024 (March), indicating a sharp spike in interest.

  • The lowest global peak hit was March 2017, an early-year low point before the trend started rising.

Insight: Global interest in pet allergies during spring has been split mainly between March and May, with occasional peaks in April. This suggests a bimodal seasonal awareness pattern, possibly linked to varying pollen/allergen patterns across hemispheres or years.

Nigeria Peak Spring Month Trends (2016–2025)

  • March was the peak month in 4 years (2016, 2018, 2020, 2021) but each time with zero hits, indicating either data sparsity or extremely low awareness during the spring Months.

  • April emerged as the dominant month in 2022, 2023, 2024 and 2025, with rising hits over time (from 2022 to 2024, then slightly dipping in 2025).

  • May only led once in 2017 an early anomaly.

  • The highest Nigerian spring hit was in 2019 April, an outlier year.

Insight: Unlike the global trend, Nigeria shows an inconsistent pattern, with zero interest in several years and occasional sharp spikes (like April 2019 and April 2024). This could reflect low baseline awareness punctuated by isolated media-driven or outbreak-linked search spikes.

Comparative Insights

Year Global Peak Month Nigeria Peak Month Key Takeaway
2019 March (207) April (300) Nigeria had more hits than globally, possibly due to a local event or news spike.
2024 March (411) April (100) Both trends surged, indicating shared seasonal drivers or aligned public health messaging.
2025 May (244) April (40) Global interest remains high, while Nigerian awareness appears to be fading again.

Key Interpretations:

  • Global Patterns: March and May are generally most problematic for pet allergies, aligning with early and late spring pollen waves in different regions. Awareness has grown, peaking in 2024.

  • Nigerian Patterns: April has become Nigeria’s “itchy month” in recent years, but the inconsistency in search interest reveals a need for sustained allergy education and awareness.

  1. Year-on-Year Spring Comparison (2015–2025)

INSIGHTS and OBSERVATIONS

Global Trends Insights

Overall Trend - Clear Upward Growth

  • 260% increase over the 10-year period

Post-2019 Spike

  • There was a notable jump in interest from 2019 onward, especially in 2022 and 2024

  • These peak years for global spring allergy awareness so far

Month-wise Patterns-

  • May emerges as the most consistently high-performing month globally, especially from 2020–2025.

  • March shows more volatility — big rise in 2024 but lower in 2025.

  • Conclusion: Global allergy awareness tends to build gradually across spring, peaking in May in most years.

Nigeria Trends Insights

Sparse & Irregular Awareness

  • Out of 30 Nigerian data points (3 months × 10 years), only 7 show any search hits.

  • Majority of years, 0 hits recorded in all three months.

No Clear Growth Trend

  • The only other moderately active years (2022 and 2024) do not show consistent follow-up.

  • 2023 and 2025 saw sharp drop-offs again, suggesting lack of sustained public awareness.

Month-Specific Awareness

  • April is Nigeria’s most active spring month (4 out of 7 non-zero entries), especially in 2019 and 2024.

  • However, March and May remain mostly inactive except in 2017 and 2019 May.

COMPARATIVE INSIGHTS

Global vs. Nigeria: Growing Divide

  • While global search interest steadily increased, Nigeria remains highly inconsistent with long gaps of zero activity.

  • Nigeria accounted for <6% of global spring search volume in 2025.

Nigerian Interest Possibly Declining Again

  • After small rebounds in 2022 and 2024, Nigeria’s numbers fell back to zero or near-zero in 2023 and 2025.

  • Suggests that public interest is not building year-over-year and awareness efforts may not be sticking.

  1. Trendline Forecasting (Spring 2026)
  1. Forecast by Keyword

INSIGHTS and OBSERVATIONS

Key Trends & Implications

1. Global Pet Allergy Concerns Are Highly Focused on Cats and Dogs

  • Strong search interest is forecasted around dog and cat allergies (esp. “pollen”, “antihistamines”, “watery eyes”).

  • Seasonal patterns dominate: keywords include “spring”, “May”, “seasonal allergies”.

  • Indicates ongoing global concern and search behavior tied to seasonal triggers in companion animals, especially cats and dogs.

2. Nigeria’s Interest Remains Limited or Focused on Specific Cases

  • Very few keywords show predicted growth in Nigeria.

  • Exceptions point to niche but potentially rising awareness (e.g., immunotherapy, cat seasonal symptoms).

  • This suggests emerging awareness of localized or advanced treatment options, even as general interest remains low.

3. Zero or Negative Forecasts for Many Keywords

  • A majority (over 70%) of keywords have zero or even negative forecasts in both regions. This indicates either:

  1. Lack of search demand or awareness

  2. Over-specificity or low keyword relevance

Keywords with Declining or Zero Forecast by 2026

Over 50 keywords show zero forecasted searches globally and in Nigeria, including:

  • cat sneezing springtime

  • dog paw licking allergies spring

  • pet allergy infographics

  • rabbit red eyes allergy

  • guinea pig seasonal allergy data

Keywords with negative forecast trends:

  • veterinary antihistamines ↓

  • dog spring allergies symptoms ↓

  • hay allergies in rabbits ↓

Key Insights

  • Global interest remains high in dog and cat allergies, especially related to pollen, antihistamines and spring symptoms.

  • Nigeria shows a shift toward advanced treatment interest (immunotherapy) and localized seasonal symptoms but most keywords drop to zero or remain niche.

  • Data voids exist in specific rodent-related terms (gerbils, hamsters, etc.) and may indicate opportunity for awareness campaigns or research expansion.

  1. Forecast by Category

INSIGHTS and OBSERVATIONS

1. Cat Allergies Will Dominate Globally and Are Gaining in Nigeria - Cats are projected to be the biggest global allergy concern by far. Nigeria shows a modest rise, indicating growing awareness but the gap remains huge.

2. Dog Allergies Are Strong Globally, But Invisible in Nigeria - Interest in dog-related allergies is significant globally but completely absent in Nigeria.

3. Guinea Pigs, Rabbits, and Birds Are Gaining Niche Attention Globally - Niche pet allergy categories are gaining ground globally. Rabbit allergies are the only “minor species” category forecasted to rise in Nigeria.

4. Veterinary and Clinical Allergy Terms Are Stable Globally and Emerging in Nigeria - Nigeria is almost on par with global interest in medical or diagnostic allergy terms (e.g. “dermatitis,” “allergy shots”).

5. Environmental & Seasonal Triggers Are Flatlined in Nigeria - Though minor globally, interest in pollen, dust, mold and grass allergies remains nonexistent in Nigeria’s forecast.

6. “Global & Seasonal Allergy Trends” and “Data & Analysis” Reflect Educational Curiosity - There’s rising global interest in informational or research-based content on allergy trends—possibly from vets, students or informed pet parents.

7. Rodent Allergy Prediction Invalid for 2026 - Likely a model anomaly or result of inconsistent historical data.

Key Takeaways:

Category Global Trend 📈 Nigeria Trend 📉
Cat-specific 🌟 Very High 🟡 Emerging
Dog-specific 🔼 High ❌ Absent
Rabbit-specific 🔼 Growing 🟡 Modest
Guinea pig / Bird-specific 🔼 Rising ❌ Absent
Clinical/Veterinary terms 🟢 Stable 🟢 Stable
Environmental triggers 🔻 Flat ❌ Absent
Research/Educational content 🔼 High ❌ Absent
Rodent-specific ❌ N/A ❌ N/A

VISUALIZATION OF ANALYSIS

  1. Allergy Search Overview
  1. Seasonal Allergy Patterns (2015–2025)
  1. Allergy Categories and Forecasts

CONCISE SUMMARY OF ANALYSIS

Insights

1. Global vs. Nigeria: A Growing Awareness Divide

  • Globally, public interest in seasonal pet allergies, especially for cats and dogs has grown steadily from 2015 to 2025.
  • Nigeria, however, shows sporadic and minimal engagement, especially during spring, the global allergy peak.

2. Search Behavior Differs by Region

  • Global users tend to search treatment-based terms (e.g., “cat antihistamines,” “immunotherapy”).
  • Nigerian users lean toward symptom-based queries (e.g., “itchy cat skin spring”), indicating a more diagnostic-seeking behavior.

3. Species Spotlight

  • Cat allergies dominate globally; rabbits and birds surprisingly top allergy concerns in Nigeria.

  • Nigeria shows notable interest in exotic or agricultural species (possibly due to smallholder farming or zoonotic concerns).

4. Environmental Allergy Triggers Under-explored in Nigeria

  • Terms like “pollen” and “dust” allergy are trending globally but remain largely absent in Nigerian searches, even during Harmattan.

5. Spring Allergy Season: Globally Consistent, Locally Inconsistent

  • Spring is the dominant season for global allergy awareness.
  • In Nigeria, April occasionally spikes but there’s no consistent seasonal pattern, suggesting awareness is event-driven not cyclical.

6. Forecast for 2026

  • Global forecasts predict continued growth in cat/dog allergy searches and interest in treatment solutions.
  • Nigeria’s forecasts are stagnant or declining, with few exceptions like “immunotherapy” showing promise.

Conclusions

  • Nigeria’s low search volume doesn’t mean pet allergies don’t exist, it may reflect:
  1. Limited internet access or low digital health engagement.
  2. Use of non-English, informal or symptom-based search terms not captured in the dataset.
  • Different environmental triggers (e.g., Harmattan vs. spring pollen) not reflected in global seasonal labels.
  • Google Trends data is relative, not absolute. Because batches were normalized separately and not anchored by a common keyword, cross-batch comparisons should be viewed cautiously.
  • Exotic pet keywords (e.g., “guinea pig bloat”) had consistent zero hits, suggesting either niche interest or poor keyword resonance in Nigeria.

Recommendations

For Public Health Communication * In Nigeria, emphasize symptom recognition before jumping to medical terminology (e.g., “Why is my cat itchy in April?”). * Localize content using regional vocabulary and search phrasing to better match user behavior. * Frame Harmattan as Nigeria’s unique “allergy season” to build more relatable seasonal awareness campaigns.

For Content Creators & Educators * Develop infographics and carousels explaining symptom–cause–treatment pathways. * Launch April-focused awareness campaigns in Nigeria, using data showing this month as the most receptive. * Use species-specific guides: cats globally, rabbits and birds for Nigeria.

For Researchers & Analysts Future studies should: * Use anchor keywords for batch standardization. * Include local languages and more symptom-based phrasing. * Consider demographic layers (urban vs. rural, farming vs. pet owners).

For Global-Nigerian Bridging * Highlight global rising trends (e.g., antihistamines, immunotherapy) in local education to foster veterinary literacy. * Translate growing global interest in evidence-based pet care into local, actionable resources (e.g., pet allergy diaries, checklists).

Objectives Met

1. Identify spring time trends in pet allergy-related searches globally and in Nigeria. The insights clearly highlight:

  • A consistent global spring peak (especially in May).
  • Inconsistent spring awareness in Nigeria, with occasional spikes in April.
  • Forecast trends for Spring 2025 and 2026 further underscore seasonal patterns and gaps.

2. Uncover species-specific patterns (dogs, cats, rabbits, birds, rodents). The summary:

  • Identifies cats and dogs as dominant globally.
  • Highlights rabbits and birds as unexpectedly prominent in Nigeria.
  • Notes near-zero or absent searches for some rodents (like guinea pigs), adding insight into regional pet ownership and concern levels.

3. Support pet owner education with actionable insights and data-backed blog content. Recommendations include:

  • Specific content formats.
  • Guidance on what to say and when to say it.
  • Localizing global trends into educational materials.

4. Strengthen SEO with seasonally relevant, high-interest keywords. The summary:

  • Surfaces key high-performing keywords.
  • Suggests localized, SEO-aligned adaptations for Nigerian audiences.
  • Recommends targeting April/Harmattan as Nigeria’s “allergy season” to tap into seasonal keyword relevance.

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